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:where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-ntlvmd5r.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-giu\" gradientUnits=\"userSpaceOnUse\" x1=\"0\" y1=\"0\" x2=\"800\" y2=\"0\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-gp\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gp-a\"\/><stop offset=\"1\" class=\"nx-gp-b\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-scene nx-es-scene\" data-scene=\"text\" data-ground=\"none\" data-es-org=\"gcri\" aria-labelledby=\"nx-scene-title\"><div class=\"nx-scene-intro nx-es-intro\"><p class=\"nx-eyebrow\"><abbr title=\"The Global Centre for Risk and Innovation\">GCRI<\/abbr> \u00b7 Labs<\/p><span class=\"nx-scene-rule\" aria-hidden=\"true\"><\/span><h1 id=\"nx-scene-title\" class=\"nx-scene-title\">Uncertainty quantification in laboratories<\/h1><p class=\"nx-scene-lede\">Uncertainty quantification asks how much confidence a result deserves, given measurement error, model structure, parameter choices and the conditions of the test. In laboratories and testbeds it connects a single experiment to a claim about performance in use, and it shows where a readiness or reproducibility claim is fragile.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/therisk.global\/nexus-labs\/write-to-us\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/nexus-labs\/stem\/\">Back to STEM foundation of Labs<\/a><\/div><\/div><\/section><section id=\"nx-s-1-sec\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-1\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">01<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Contents<\/span><\/div><h2 id=\"nx-s-1\" class=\"nx-section-title\">Uncertainty quantification in laboratories in five parts.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-es-fig\" data-es-layout=\"protocol\"><div class=\"nx-hero-panel\"><div class=\"nx-lv-steps\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Protocol<\/strong><\/span><span class=\"nx-eyebrow\">5 steps<\/span><\/div><ol><li><a href=\"#nx-sec-application\"><span class=\"nx-lv-steps-n\">01<\/span><span class=\"nx-lv-steps-t\">Application<\/span><span class=\"nx-lv-steps-d\">What uncertainty quantification and model validation does here<\/span><\/a><\/li><li><a href=\"#nx-sec-data\"><span class=\"nx-lv-steps-n\">02<\/span><span class=\"nx-lv-steps-t\">Data<\/span><span class=\"nx-lv-steps-d\">What the method consumes<\/span><\/a><\/li><li><a href=\"#nx-sec-checks\"><span class=\"nx-lv-steps-n\">03<\/span><span class=\"nx-lv-steps-t\">Validation<\/span><span class=\"nx-lv-steps-d\">How results are checked<\/span><\/a><\/li><li><a href=\"#nx-sec-connections\"><span class=\"nx-lv-steps-n\">04<\/span><span class=\"nx-lv-steps-t\">Connections<\/span><span class=\"nx-lv-steps-d\">Topics that rely on this method<\/span><\/a><\/li><li><a href=\"#nx-sec-stewardship\"><span class=\"nx-lv-steps-n\">05<\/span><span class=\"nx-lv-steps-t\">Stewardship<\/span><span class=\"nx-lv-steps-d\">Who maintains the method<\/span><\/a><\/li><\/ol><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-application\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Application<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">What uncertainty quantification and model validation does here.<\/h2><p class=\"nx-lede\">Uncertainty quantification and model validation characterise how uncertainty in inputs, parameters and model structure affects predictions. They establish how much confidence a model&#8217;s outputs deserve. In research and evidence, methods and modelling and innovation, uncertainty quantification shows where risk estimates are robust and where they are fragile. It guides where better data or models would change decisions. The canonical description is on <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\">the STEM Commons<\/a>.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cols\"><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"m12.83 2.18a2 2 0 0 0-1.66 0L2.6 6.08a1 1 0 0 0 0 1.83l8.58 3.91a2 2 0 0 0 1.66 0l8.58-3.9a1 1 0 0 0 0-1.83Z\"\/><path d=\"m22 17.65-9.17 4.16a2 2 0 0 1-1.66 0L2 17.65\"\/><path d=\"m22 12.65-9.17 4.16a2 2 0 0 1-1.66 0L2 12.65\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Measurement uncertainty budgets<\/h3><p class=\"nx-p nx-mt-1\">Each source of error in an instrument chain is estimated and combined following the Guide to the Expression of Uncertainty in Measurement, so results from different laboratories can be compared on a common basis.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M22 12h-2.48a2 2 0 0 0-1.93 1.46l-2.35 8.36a.25.25 0 0 1-.48 0L9.24 2.18a.25.25 0 0 0-.48 0l-2.35 8.36A2 2 0 0 1 4.49 12H2\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Sensitivity analysis of simulation models<\/h3><p class=\"nx-p nx-mt-1\">Inputs are varied systematically to find which drive the output range, so validation effort and further testing go where they would change a conclusion.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M20 13c0 5-3.5 7.5-7.66 8.95a1 1 0 0 1-.67-.01C7.5 20.5 4 18 4 13V6a1 1 0 0 1 1-1c2 0 4.5-1.2 6.24-2.72a1.17 1.17 0 0 1 1.52 0C14.51 3.81 17 5 19 5a1 1 0 0 1 1 1z\"\/><path d=\"m9 12 2 2 4-4\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Validation against independent experiments<\/h3><p class=\"nx-p nx-mt-1\">Model predictions are compared with experiments not used in calibration, with agreement metrics and the domain of validity stated before the model is used for design.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-s-3-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Monitor<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Uncertainty analysis.<\/h2><p class=\"nx-lede\">The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-fk nx-cx\" data-fk=\"cx-labs-method-uq\" data-cx=\"labs.method-uq\" data-variant=\"section\" aria-label=\"Uncertainty analysis: illustrative console with example names and values\"><div class=\"nx-fk-head\"><div class=\"nx-fk-heading\"><h3 class=\"nx-fk-title\">Uncertainty analysis<\/h3><p class=\"nx-fk-sub\">Labs \u00b7 Uncertainty quantification \u00b7 Run R-1480<\/p><\/div><div class=\"nx-fk-actions\"><button type=\"button\" class=\"nx-fk-btn\" data-fk-act=\"menu\" aria-haspopup=\"true\" aria-expanded=\"false\" hidden><svg viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M12 4v11\"\/><path d=\"m7 10 5 5 5-5\"\/><path d=\"M5 20h14\"\/><\/svg>Export<\/button><ul class=\"nx-fk-menu\" data-fk-menu=\"true\" hidden><li><button type=\"button\" data-fk-act=\"export-png\">Image<small>PNG of the map or chart<\/small><\/button><\/li><li><button type=\"button\" data-fk-act=\"export-csv\">Data<small>CSV of the figure&#8217;s values<\/small><\/button><\/li><\/ul><\/div><\/div><div class=\"nx-cx-status\"><span class=\"nx-fk-pill\" data-state=\"info\"><i aria-hidden=\"true\"><\/i>Sensitivity ranked<\/span><span class=\"nx-cx-prod\">GCRI \u00b7 Labs<\/span><\/div><div class=\"nx-cx-grid\"><div class=\"nx-fk-panel nx-cx-p\" data-w=\"3\" data-t=\"chart\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Total-order sensitivity<\/h4><\/div><\/div><svg class=\"nx-fk-svg\" viewBox=\"0 0 520 210\" width=\"520\" height=\"210\" role=\"img\" focusable=\"false\" preserveAspectRatio=\"xMidYMid meet\" aria-label=\"Total-order sensitivity\" data-fk-chart=\"cx-labs-method-uq-c0\" data-nx-z=\"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\"><\/svg><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"3\" data-t=\"chart\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Output distribution<\/h4><\/div><\/div><svg class=\"nx-fk-svg\" viewBox=\"0 0 460 210\" width=\"460\" height=\"210\" role=\"img\" aria-label=\"Output distribution\" focusable=\"false\" tabindex=\"0\" preserveAspectRatio=\"xMidYMid meet\" data-fk-chart=\"cx-labs-method-uq-c1\" data-fk-xs=\"lin,0,23,36,446\" data-fk-ys=\"lin,0,100,180,20\" data-fk-series=\"Median|acc|l||0:50.150786691345274;1:50.51786879487336;2:49.3321391905658;3:54.05408327486366;4:50.90506027275696;5:52.94863500259817;6:52.24961973484606;7:57.48641946939752;8:56.98064903793856;9:57.82075408762321;10:57.99018588103354;11:59.62751546306536;12:60.090663973987105;13:64.13670514682308;14:62.81962696379051;15:63.01676874095574;16:65.92701868703588;17:66.90031170872972;18:67.23465838162228;19:71.17605833578855;20:70.0797337051481;21:74.49992441935464;22:71.27100000884384;23:71.68540076231584~Sample spread, upper|s3|l||0:56.150786691345274;1:58.71786879487336;2:59.732139190565796;3:66.65408327486367;4:65.70506027275697;5:69.94863500259817;6:71.44961973484605;7:78.88641946939752;8:80.58064903793857;9:83.62075408762321;10:85.99018588103354;11:89.82751546306537;12:92.49066397398711;13:98.73670514682308;14:99;15:99;16:99;17:99;18:99;19:99;20:99;21:99;22:99;23:99~Sample spread, lower|s3|l||0:44.150786691345274;1:42.51786879487336;2:39.3321391905658;3:42.05408327486366;4:36.90506027275696;5:36.94863500259817;6:34.24961973484606;7:37.48641946939752;8:34.98064903793856;9:33.82075408762321;10:31.99018588103354;11:31.627515463065357;12:30.090663973987105;13:32.136705146823076;14:28.819626963790512;15:27.01676874095574;16:27.927018687035883;17:26.900311708729717;18:25.234658381622282;19:27.176058335788554;20:24.0797337051481;21:26.49992441935464;22:21.271000008843842;23:19.685400762315837\" data-nx-z=\"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\"><\/svg><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"6\" data-t=\"table\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Inputs<\/h4><\/div><\/div><div class=\"nx-fk-tablewrap\" data-fk-view=\"table:cx-labs-method-uq-t2\"><table class=\"nx-fk-table\" data-fk-table=\"cx-labs-method-uq-t2\" aria-label=\"Inputs\"><thead><tr><th scope=\"col\" data-col=\"c0\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c0\">Input<\/button><\/th><th scope=\"col\" data-col=\"c1\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c1\">Distribution<\/button><\/th><th scope=\"col\" data-col=\"c2\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c2\">Range<\/button><\/th><th scope=\"col\" data-col=\"c3\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c3\">Source<\/button><\/th><\/tr><\/thead><tbody><tr data-fk-key=\"cx-labs-method-uq:r2-0\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Protocols registered<\/td><td>Uniform<\/td><td>protocols, stated<\/td><td>expert elicitation<\/td><\/tr><tr data-fk-key=\"cx-labs-method-uq:r2-1\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Studies with preregistration<\/td><td>Lognormal<\/td><td data-sv=\"0\"> of studies, stated<\/td><td>expert elicitation<\/td><\/tr><tr data-fk-key=\"cx-labs-method-uq:r2-2\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Replications attempted<\/td><td>Uniform<\/td><td>studies, stated<\/td><td>expert elicitation<\/td><\/tr><tr data-fk-key=\"cx-labs-method-uq:r2-3\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Negative results published<\/td><td>Uniform<\/td><td>papers, stated<\/td><td>expert elicitation<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><div class=\"nx-fk-tip\" aria-hidden=\"true\" hidden><\/div><figcaption class=\"nx-fk-cap\"><span>Uncertainty analysis: the screen&#8217;s structure, terms and states for Labs.<\/span><span data-honesty=\"true\">Example names and illustrative values; a design, not a record or a live system.<\/span><\/figcaption><\/figure><\/div><\/div><\/section><section id=\"nx-sec-data\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"inset\" aria-labelledby=\"nx-s-4\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">04<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Data<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">What the method consumes.<\/h2><p class=\"nx-lede\">Datasets and records used by uncertainty quantification and model validation on this platform, with the role each plays and the label its values carry. Sources are listed in <a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/research\/\">the source register<\/a>.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Data<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Data\">Calibration certificates<\/td><td data-label=\"Role\">Instrument uncertainty<\/td><td data-label=\"Label\"><span class=\"nx-chip\">Published statistics<\/span><\/td><\/tr><tr><td data-label=\"Data\">Inter-laboratory comparison results<\/td><td data-label=\"Role\">Reproducibility between laboratories<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Model ensembles<\/td><td data-label=\"Role\">Structural and parameter spread<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Structured <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 expert-elicitation\" data-tk=\"Nexus risk vocabulary\" data-td=\"A structured process for obtaining and combining judgements from experts about uncertain quantities, with attention to bias and calibration.\" data-ts=\"NRS-C1\" data-tr=\"draft\">expert elicitation<\/a><\/td><td data-label=\"Role\">Ranges where no data exist<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"data\" data-nxf-kind=\"data\" data-nxf-resource=\"nexus:\/\/gcri\/G08\/stem\/uncertainty-quantification\/#data\" data-nxf-title=\"Method record\" data-nxf-height=\"520\" hidden><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gcri\/G08\/stem\/uncertainty-quantification\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-audience\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-5\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods and evidence<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">How uncertainty quantification in laboratories is modelled, measured and tested.<\/h2><p class=\"nx-lede\">The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-au\" data-au-voice=\"science\" data-au-n=\"1\"><figure class=\"nx-au-c\" data-au-fig=\"method-panel\"><h3 class=\"nx-h4\">Model, equations and assumptions<\/h3><p class=\"nx-p nx-au-what\">The model structure used for uncertainty quantification in laboratories, the assumptions it makes and how each is tested.<\/p><div class=\"nx-au-eqs\"><div class=\"nx-au-eq\"><span class=\"nx-au-k\">Risk of element i<\/span><code>R<sub>i<\/sub> = f(H<sub>i<\/sub>, E<sub>i<\/sub>, V<sub>i<\/sub>, C<sub>i<\/sub>)<\/code><span class=\"nx-au-n\">hazard, exposure, vulnerability and coping capacity<\/span><\/div><div class=\"nx-au-eq\"><span class=\"nx-au-k\">Expected annual loss<\/span><code>EAL = &#8721;<sub>k<\/sub> p<sub>k<\/sub> &#183; L<sub>k<\/sub><\/code><span class=\"nx-au-n\">over events k with annual probability p<sub>k<\/sub><\/span><\/div><div class=\"nx-au-eq\"><span class=\"nx-au-k\">Coupled systems<\/span><code>x<sub>t+1<\/sub> = A x<sub>t<\/sub> + B u<sub>t<\/sub> + &#949;<sub>t<\/sub><\/code><span class=\"nx-au-n\">A carries the couplings, u the shock, &#949; the error<\/span><\/div><\/div><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Assumption<\/th><th scope=\"col\">Why it is made<\/th><th scope=\"col\">How it is tested<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Assumption\">Hazard and exposure are independent within an event<\/td><td data-label=\"Why it is made\">Keeps the loss integral tractable<\/td><td data-label=\"How it is tested\">Compare with a copula fit on joint records<\/td><\/tr><tr><td data-label=\"Assumption\">Couplings are linear near the operating point<\/td><td data-label=\"Why it is made\">Lets shocks propagate through A<\/td><td data-label=\"How it is tested\">Perturb beyond the linear range and compare<\/td><\/tr><tr><td data-label=\"Assumption\">Vulnerability curves transfer between places<\/td><td data-label=\"Why it is made\">Local curves are rarely available<\/td><td data-label=\"How it is tested\">Validate against observed losses where they exist<\/td><\/tr><\/tbody><\/table><\/div><dl class=\"nx-au-kv\"><div><dt>Inputs<\/dt><dd>versioned datasets with provenance<\/dd><\/div><div><dt>Outputs<\/dt><dd>distributions, never single numbers<\/dd><\/div><div><dt>Code<\/dt><dd>released with the model card and tests<\/dd><\/div><div><dt>Reproduction<\/dt><dd>same data, code, environment and seed<\/dd><\/div><\/dl><figcaption class=\"nx-caption\"><span>Standard risk and state-space formulations; the assumptions listed are typical of the method, stated for review, not results.<\/span><\/figcaption><\/figure><\/div><div class=\"nx-chips nx-au-fw\" aria-label=\"Frameworks cited\"><span class=\"nx-chip\">FAIR guiding principles \u00b7 GO FAIR and FORCE11<\/span><span class=\"nx-chip\">W3C PROV \u00b7 World Wide Web Consortium<\/span><span class=\"nx-chip\">Guidance on consistent treatment of uncertainties \u00b7 IPCC<\/span><span class=\"nx-chip\">CEOS Analysis Ready Data \u00b7 Committee on Earth Observation Satellites<\/span><\/div><\/div><\/div><\/section><section id=\"nx-sec-checks\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"stack\" aria-labelledby=\"nx-s-6\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">06<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Validation<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">How results are checked.<\/h2><p class=\"nx-lede\">Predictive distributions are tested for calibration and sharpness against held-out observations. Verification confirms that code solves the intended equations.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Check<\/th><th scope=\"col\">How it is done<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Check\">Code verification<\/td><td data-label=\"How it is done\">Solvers are tested against problems with known solutions before validation begins<\/td><\/tr><tr><td data-label=\"Check\">Out-of-sample validation<\/td><td data-label=\"How it is done\">Predictions are scored on data withheld from calibration<\/td><\/tr><tr><td data-label=\"Check\">Interval coverage<\/td><td data-label=\"How it is done\">Stated intervals are checked to contain outcomes at their nominal rate<\/td><\/tr><tr><td data-label=\"Check\">Domain of validity<\/td><td data-label=\"How it is done\">Use outside the tested conditions is flagged, not extrapolated silently<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nx-note nx-mt-3\">Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.<\/p><\/div><\/div><\/section><section id=\"nx-s-7-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-7\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Process<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">How the process runs.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><div class=\"nx-pe\" id=\"nx-pe-ipcc-risk\" data-nx-pe=\"nx-pe-ipcc-risk\" data-active=\"ipcc-risk\"><div class=\"nx-pe-shell\"><p class=\"nx-pe-eb\">Process<\/p><h2 class=\"nx-pe-title\">Uncertainty quantification in laboratories: Hazard, exposure, vulnerability<\/h2><p class=\"nx-pe-cap\">In IPCC AR6 Working Group II, climate risk results from interactions between hazards and a system&#8217;s exposure and vulnerability, and responses can add risk.<\/p><ol class=\"nx-pe-steps\"><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">01 Drivers<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">01<\/span><span class=\"nx-pe-st\">Hazard<\/span><span class=\"nx-pe-k\">Dataset \u00b7 Principal investigator<\/span><span class=\"nx-pe-sm\">The potential occurrence of a natural or human-induced physical event or trend that may harm people, property, services or ecosystems.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">02<\/span><span class=\"nx-pe-st\">Exposure<\/span><span class=\"nx-pe-k\">Dataset \u00b7 Principal investigator<\/span><span class=\"nx-pe-sm\">The presence of people, livelihoods, species, ecosystems, infrastructure or assets in places and settings that could be adversely affected.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">03<\/span><span class=\"nx-pe-st\">Sensitivity<\/span><span class=\"nx-pe-k\">Dataset \u00b7 Principal investigator<\/span><span class=\"nx-pe-sm\">The degree to which a system or species is affected, either adversely or beneficially, by <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 climate-variability\" data-tk=\"Nexus risk vocabulary\" data-td=\"Fluctuations of the climate around its average state at all time scales beyond individual weather events, including modes that recur over seasons to decades.\" data-ts=\"NRS-C1\" data-tr=\"draft\">climate variability<\/a> or change.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">04<\/span><span class=\"nx-pe-st\">Capacity to cope and adapt<\/span><span class=\"nx-pe-k\">Dataset \u00b7 Principal investigator<\/span><span class=\"nx-pe-sm\">The ability of <a class=\"nx-link nx-term\" href=\"https:\/\/docs.therisk.global\/organization\/systems\" data-tc=\"Systems\" data-tk=\"Nexus Standards\" data-td=\"The twenty Nexus systems, NXS-CMD to NXS-LAB\">systems<\/a>, institutions, humans and other organisms to adjust to damage, take opportunities or respond to consequences.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">02 Assessment<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">05<\/span><span class=\"nx-pe-st\">Risk from responses<\/span><span class=\"nx-pe-k\">Component \u00b7 Principal investigator<\/span><span class=\"nx-pe-sm\">Risks from climate responses arise when responses miss their objectives, or from trade-offs or negative side effects.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">06<\/span><span class=\"nx-pe-st\">Vulnerability<\/span><span class=\"nx-pe-k\">Component \u00b7 Principal investigator<\/span><span class=\"nx-pe-sm\">The propensity or predisposition to be adversely affected. It includes sensitivity to harm and lack of capacity to cope and adapt.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">07<\/span><span class=\"nx-pe-st\">Risk<\/span><span class=\"nx-pe-k\">Model \u00b7 <a class=\"nx-link nx-term\" href=\"https:\/\/docs.therisk.global\/organization\/programme\/labs\" data-tc=\"Labs\" data-tk=\"Nexus Standards\" data-td=\"The research pillar of the programme, one of six (OR-11, OR-12)\">Labs<\/a><\/span><span class=\"nx-pe-sm\">Risk results from dynamic interactions between hazards and the system&#8217;s exposure and vulnerability. Each may be uncertain and change.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">03 Response<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">08<\/span><span class=\"nx-pe-st\">Risk management and response<\/span><span class=\"nx-pe-k\">Step \u00b7 Principal investigator<\/span><span class=\"nx-pe-sm\">Risk management is plans, actions, strategies or policies that reduce the likelihood or size of adverse consequences.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">09<\/span><span class=\"nx-pe-st\"><a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 residual-risk\" data-tk=\"Nexus risk vocabulary\" data-td=\"The risk that remains after controls, mitigation and transfer have been applied. It must be known, owned and either accepted or further treated.\" data-ts=\"NRS-C1\" data-tr=\"draft\">Residual risk<\/a><\/span><span class=\"nx-pe-k\">Record \u00b7 Principal investigator<\/span><span class=\"nx-pe-sm\">The climate impact risk that remains after adaptation and mitigation efforts. Adaptation can redistribute risk between areas and groups.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">04 Decision<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">10<\/span><span class=\"nx-pe-st\">Decision by the responsible party<\/span><span class=\"nx-pe-k\">Decision \u00b7 Principal investigator<\/span><span class=\"nx-pe-sm\">The framing names no single decision maker. Decisions on responses rest with the responsible party under its own rules.<\/span><\/li><\/ol><\/li><\/ol><p class=\"nx-pe-foot\"><span>Outline of an external standard or method, from its public sources. It describes what the framework says, not a record of any run. It does not show that Nexus or any partner performs, certifies or approves a step. A gate is a step of the method, never a Nexus approval.<\/span><\/p><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-connections\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-8\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connections<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">Topics that rely on this method.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\" data-nav=\"true\"><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/research-protocols\/\"><span>Research protocols<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/field-and-lab-testbeds\/\"><span>Field and lab testbeds<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/ai-and-scientific-workflows\/\"><span>AI in scientific workflows<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-work-lab\/\"><span>Future of work lab<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-media-lab\/\"><span>Future of media lab<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-energy-and-water-labs\/\"><span>Future of energy and water labs<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-health-and-bio-labs\/\"><span>Future of health and biotechnology labs<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/reproducibility-and-negative-results\/\"><span>Reproducibility and negative results<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-stewardship\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-9\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Stewardship<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Who maintains the method.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/systems-modelling\/\">Systems Science and Modelling<\/a> stewards uncertainty quantification and model validation for the Consortium and reviews how it is applied across platforms.<\/p><\/div><div class=\"nx-section-body\"><p class=\"nx-p\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\">Canonical page: Uncertainty quantification and model validation<\/a><\/p><\/div><\/div><\/section><section id=\"nx-s-10-sec\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-10\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connected<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">Where this page leads across the Consortium.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-dest\"><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/systems-modelling\/stem\/uncertainty-quantification\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Steward platform<\/span><span class=\"nx-dest-host\">GCRI<\/span><\/span><span class=\"nx-dest-title\">Uncertainty quantification<\/span><span class=\"nx-dest-why\">Systems Science and Modelling stewards this method.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-s-11-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-11\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">11<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What are the limits of uncertainty quantification and model validation in this field?<\/summary><div class=\"nx-answer\">Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.<\/div><\/details><details><summary>How are results checked?<\/summary><div class=\"nx-answer\">Predictive distributions are tested for calibration and sharpness against held-out observations. Verification confirms that code solves the intended equations.<\/div><\/details><details><summary>Are the values on this page real?<\/summary><div class=\"nx-answer\">The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method&#8217;s use, not a run; no result is reported here.<\/div><\/details><details><summary>Where else is this method applied?<\/summary><div class=\"nx-answer\">The <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\">canonical method page<\/a> lists every platform that applies it.<\/div><\/details><\/div><\/div><\/div><\/section><section id=\"nx-sec-nr-gateway\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-nr-gateway-h\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">Reports<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Contribute<\/span><\/div><h2 id=\"nx-nr-gateway-h\" class=\"nx-section-title\">Contribute evidence from this page.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/docs.therisk.global\/organization\/programme\/reports\">Reports<\/a> is the Consortium&#8217;s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.<\/p><div class=\"nx-chips\"><span class=\"nx-chip\">GCRI<\/span><span class=\"nx-chip\">Labs<\/span><span class=\"nx-chip\">Labs Evidence<\/span><span class=\"nx-chip\">Methods and Technical<\/span><\/div><\/div><div class=\"nx-section-body\"><ul class=\"nx-dest\"><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/nexus-labs\/write-to-us\/contact\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Email<\/span><span class=\"nx-dest-host\">Gateway GW-GCRI<\/span><\/span><span class=\"nx-dest-title\">Send it from this page<\/span><span class=\"nx-dest-why\">Evidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/nexus-reports\/open-science\/#nx-sec-zenodo\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Zenodo<\/span><span class=\"nx-dest-host\">Open deposit<\/span><\/span><span class=\"nx-dest-title\">Deposit it and submit to the community<\/span><span class=\"nx-dest-why\">Publish your own record on Zenodo with your ORCID iD, then submit it to the Reports community there. 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