Assisted screening for systematic reviews
Classifiers prioritise records for human screening; recall is measured against a fully screened sample, and the stopping rule is stated in the review protocol before screening starts.
GCRI · Labs
Machine learning in laboratory science
Machine learning is both a tool and an object of study in this lab. It assists literature screening, experimental design, image and sequence analysis and simulation, and it needs its own evaluation discipline: leakage, distribution shift and opaque failure modes are research risks to be tested, not assumed away.
01Contents
Machine learning in laboratory science in five parts.
02Application
What artificial intelligence and machine learning does here.
Artificial intelligence and machine learning develop algorithms that learn patterns from data to classify, predict or generate outputs. They are increasingly used for hazard detection, forecasting and decision support, and are themselves a source of risk. In research and evidence, methods and modelling and innovation, machine learning can scale detection and forecasting across interconnected systems. Its own failure modes must be weighed when automated outputs inform consequential decisions. The canonical description is on the STEM Commons.
Surrogate models to guide experiments
Models trained on simulations or past runs suggest which experiments to run next, using uncertainty to balance exploration and confirmation, and every suggestion is tested physically before it counts.
Image and signal analysis in the lab
Segmentation and classification models are validated against expert annotation from other sites and instruments, with failure cases catalogued and shared alongside the model.
03Monitor
Model training.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Model training
Labs · Machine learning in laboratory · Run R-4300
Drift on one sliceGCRI · Labs
Evaluation by slice
| All | 0.79 | 0.04 | 7294 | Pass |
| Region A | 0.84 | 0.04 | 1004 | Review |
| Region B | 0.79 | 0.12 | 594 | Review |
| Rare class | 0.67 | 0.14 | 67 | Below bar |
Method card
- Method
- Artificial intelligence and machine learning
- Split
- time-based, no leakage
- Features
- 22
- Label
- MODELLED
Population stability
04Data
What the method consumes.
Datasets and records used by artificial intelligence and machine learning on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Annotated benchmark datasets | Training and evaluation | REPORTED |
| Laboratory instrument outputs | Inputs to analysis models | OBSERVED |
| Simulation archives | Training surrogate models | MODELLED |
| Model and dataset cards | Intended use and known limits | DECLARED |
05Methods and evidence
How machine learning in laboratory science is modelled, measured and tested.
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.
Model, equations and assumptions
The model structure used for machine learning in laboratory science, the assumptions it makes and how each is tested.
Risk of element i
Ri = f(Hi, Ei, Vi, Ci)hazard, exposure, vulnerability and coping capacityExpected annual loss
EAL = ∑k pk · Lkover events k with annual probability pkCoupled systems
xt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Assumption | Why it is made | How it is tested |
|---|---|---|
| Hazard and exposure are independent within an event | Keeps the loss integral tractable | Compare with a copula fit on joint records |
| Couplings are linear near the operating point | Lets shocks propagate through A | Perturb beyond the linear range and compare |
| Vulnerability curves transfer between places | Local curves are rarely available | Validate against observed losses where they exist |
- Inputs
- versioned datasets with provenance
- Outputs
- distributions, never single numbers
- Code
- released with the model card and tests
- Reproduction
- same data, code, environment and seed
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
06Validation
How results are checked.
Models are evaluated on held-out data that respects spatial, temporal and group structure. Robustness, calibration and fairness are tested alongside accuracy.
| Check | How it is done |
|---|---|
| Leakage audit | Splits are made by subject, site or time as the question requires, and preprocessing is fitted on training data only |
| External validation | Performance is reported on data from a different source or period |
| Calibration and robustness | Probabilities are checked for calibration, and performance under plausible perturbations is reported |
| Reporting checklist | Studies are reported against REFORMS, TRIPOD+AI or the relevant domain checklist |
Performance degrades when deployment conditions differ from training data. Models can encode biases and fail without clear warning on rare events.
07Process
How the process runs.
Process
Machine learning in laboratory science: AI risk management (NIST)
The AI RMF Core has four functions, GOVERN, MAP, MEASURE and MANAGE, with GOVERN cross-cutting; the order drawn is one common reading, not a checklist.
01 Govern
- 01GOVERN: policies, roles and accountabilityStep · Principal investigatorGOVERN is a cross-cutting function. It is infused throughout AI risk management and enables the other functions.
02 Map
- 02MAP: context and intended useDatasetThe MAP function establishes the context to frame risks related to an AI system, including intended purposes and impacts on people.
03 Measure
- 03MEASURE: evaluate trustworthy characteristicsModelAI systems are evaluated for trustworthy characteristics. They should be tested before deployment and regularly in operation.
- 04MEASURE: track identified risks over timeModelMechanisms for tracking identified AI risks over time are in place, including unanticipated and emergent risks.
04 Manage
- 05MANAGE: treat risksStepRisk treatment plans cover response to, recovery from and communication about incidents. Resources go to mapped and measured risks.
- 06Proceed or not (MANAGE 1.1)Gate · Principal investigatorA determination is made whether the system meets its purposes and objectives, and whether to proceed. The framework sets no thresholds.
- 07Deployment decisionDecision · Principal investigatorThe organisation that owns the system decides whether and under what conditions to deploy.
- 08Post-deployment monitoring and incidentsModelPost-deployment monitoring plans are implemented. Incidents and errors are shared with relevant AI actors, including affected communities.
05 Record
- 09Inventory of AI systemsDatasetGOVERN 1.6 asks for mechanisms to inventory AI systems. A model card is common practice and is not named in the framework.
Neutral outline of a published external standard or method, from its issuing body. Not a record of operation. Nexus does not perform, certify or approve any step shown. A gate or decision belongs to the party the source names. The actions are not necessarily an ordered set of steps. The EU AI Act is not drawn in this flow.
08Connections
Topics that rely on this method.
09Stewardship
Who maintains the method.
Artificial Intelligence and Autonomous Systems stewards artificial intelligence and machine learning for the Consortium and reviews how it is applied across platforms.
10Connected
Where this page leads across the Consortium.
11Questions
Questions and answers.
What are the limits of artificial intelligence and machine learning in this field?
Performance degrades when deployment conditions differ from training data. Models can encode biases and fail without clear warning on rare events.
How are results checked?
Models are evaluated on held-out data that respects spatial, temporal and group structure. Robustness, calibration and fairness are tested alongside accuracy.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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