GCRI · Zîrekiya çêkirî û pergalên xweser
Zîrekiya çêkirî di zanistê de
Machine learning now predicts protein structures, emulates weather and climate models, screens materials and helps analyse vast datasets. It can speed discovery, and it can also produce results that are hard to reproduce, overfit or rest on data leakage. This page sets out how AI is used in science, where it fails, how its results are checked, and what evidence serves the researchers, funders and journals involved.
AI in science in seven parts.
- 01The questionAI in science: how the risk develops
- 02Li seranserê qadanAI changes how each domain’s science is done
- 03NîşanderWhat is measured, and how well
- 04ÇavkanîWhere the evidence comes from
- 05BiryarWho decides, and what the evidence serves
- 06Core questionsWhat scientists ask about AI in research
- 07MechanismsHow discovery and error both scale
AI in science: how the risk develops.
Discovery and reproducibility. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Learning from data replaces some simulation
Models trained on simulations or observations can emulate expensive calculations quickly. They are reliable within the range of their training data and uncertain outside it.
Leakage inflates results
When information from the test set reaches training, through duplicates, time overlap or preprocessing, reported accuracy overstates real performance. Leakage is a known cause of irreproducible ML science.
Openness enables checking
Results can be verified only when code, data, models and settings are shared. Proprietary models and data limit independent checks.
Scientists stay accountable
AI can suggest hypotheses and draft text, but researchers remain responsible for methods, interpretation and integrity. Journals and funders are setting rules for AI use.
Tomara modelan.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Tomara modelan
AI in science · Model registry · Release gate
Model
| M-489 triage assistant | Xizmeta giştî | Bilind | 20 of 24 suites | Hatiye asteng kirin |
| M-530 flood nowcast | Zanist | Navîn | 12 of 12 | Hatiye weşandin |
| M-079 document search | Navxweyî | Kêm | 8 of 8 | Hatiye weşandin |
| M-881 agent planner | Ajanî | Bilind | 11 of 16 | Tê nirxandin |
Karta modelê
- Bikaranîna armanckirî
- piştgiriya hilbijartina pêşîniyê, mirov biryarê dide
- Li derveyî çarçoveyê
- biryarên mafdariyê
- Sînorên naskirî
- rastbûna kêmtir li ser rewşên kêm
- Xwedî
- Xwediyê modelê
Têketina bûyeran
- D-19INCÇavkaniya xeyalî di kurteyê de
- D-8MITBingehkirina vegerandinê hat zêdekirin
- D-3INCDerzîya fermanê bi pelê barkirî
- D-1MITDestûra amûrê hate teng kirin
Evaluation scores
vs release threshold
AI changes how each domain’s science is done.
- AvGirêdanML models now rival process models in some prediction tasks.What to watchBenchmarks against process models
- TenduristîGirêdanAI supports protein and pathogen research, raising dual-use questions.What to watchBiosecurity review of tools
- Cûrbecûrbûna jîndaranGirêdanAutomated species identification scales monitoring.What to watchIdentification accuracy by region
Studies of AI in science are designed for the rêzeya Rêbaz û Teknîkî. Reproducibility requires code, data and model versions, recorded under NS-12 of the proposed Nexus Standards.
- Replications of AI-based results.
- Benchmarks with documented data.
How AI in science is modelled, measured and tested.
Model, texmînên wî, daneyên wî û nediyariya wî, bi şeweyê ku zanyar an endezyar kontrol bike. Avahî rêbazên weşandî dişopînin. Nirxên hatine xêzkirin nimûneyî ne, ne encam in.
Model, hevkêşe û texmîn
The model structure used for AI in science, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)metirsî, rûbirûbûn, lawazî û kapasîteya serederiyêEAL = ∑k pk · Lkli ser bûyerên k bi îhtîmala salane pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Texmîn | Çima tê çêkirin | Çawa tê ceribandin |
|---|---|---|
| Metirsî û rûbirûbûn di nav bûyerekê de serbixwe ne | Entegrala windahiyê ji hesabkirinê re maqûl dihêle | Bi lihevhatineke copula li ser tomarên hevbeş re bidin ber hev |
| Girêdan li nêzî xala xebatê xêzik in | Dihêle şok di A de belav bibin | Ji derveyî rêza xêzik tevlihev bike û bide ber hev |
| Xêzên lawaziyê di navbera cihan de têne veguhastin | Xêzikên herêmî kêm tên dîtin | Li hember windahiyên çavdêrîkirî erê bike li cihê ku hene |
- Têketin
- danehevên bi guhertoyan û çavkaniyê
- Derketin
- belavkirin, tu carî jimarên tenê
- Kod
- bi qerta modelê û ceribandinan re hat weşandin
- Zêdebûn
- heman dane, kod, jîngeh û tov
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Nîşan |
|---|---|---|---|---|
| Studies sharing code and data | percent of published ML studies | per review | Publication audits | ÇAVDÊRÎKIRÎ |
| Leakage issues identified in reviews | count by type | per review | Reproducibility studies | REPORTED |
| Out-of-distribution performance | error on held-out regimes | per evaluation | Nirxandina modelê | ÇAVDÊRÎKIRÎ |
| Emulator error against reference simulation | error metric | per evaluation | Validation runs | ÇAVDÊRÎKIRÎ |
| Journals with AI use policies | count | annual | Publisher policies | DECLARED |
| Compute used for scientific model training | floating point operations | per model | Model documentation | TEXMÎNKIRÎ |
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Çavkanî | What it gives | Licence class |
|---|---|---|
| Protein Data Bank and AlphaFold Protein Structure Database | Experimental and predicted protein structures | Open, CC0 and CC BY 4.0 respectively |
| WeatherBench and related benchmarks | Standard evaluation of data-driven weather models | Open, licence per benchmark (to check) |
| Princeton reproducibility studies on ML-based science | Survey of leakage and reproducibility failures | Licence per publication |
| Materials Project | Computed materials properties for screening | Open, CC BY 4.0 |
| Publisher and funder policies on AI use | Rules on authorship, disclosure and review | Public, terms per publisher |
Who decides, and what the evidence serves.
Zîrekiya çêkirî û Pergal prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Kî biryarê dide | The question | Evidence this page serves |
|---|---|---|
| Research teams | Whether an ML result is robust enough to publish | Leakage checklists; validation protocols |
| Journals and reviewers | What evidence to require for ML-based claims | Reporting guidance; reproducibility evidence |
| Research funders | How to fund open models, data and compute for science | Openness and compute evidence |
Outputs this topic produces
- Leakage and reproducibility checklists for ML studies
- Validation protocols for scientific emulators
- Guidance on reporting AI use in research
- Case studies of AI in specific sciences
What scientists ask about AI in research.
- Does the AI-assisted result hold when tested independently, by experiment or observation?
- Is the model, data and code available so others can reproduce it?
- Has data leakage between training and evaluation inflated reported performance?
- Does the model extrapolate beyond the conditions its training data covered?
How discovery and error both scale.
Machine learning finds patterns in large datasets faster than people can, and surrogate models can emulate expensive simulations, allowing many more scenarios to be explored. In fields with good data and clear validation, such as structural biology, the gains have been substantial.
The risks are equally systematic. Leakage between training and test data has produced inflated results across many disciplines. Models trained on past data may not respect physical laws when extrapolating. Generative pergalan can fabricate plausible references or data. And when models and data are proprietary, results cannot be checked.
Keeping AI-assisted science reproducible.
- 01
Reporting standards
Checklists covering data splits, leakage checks, baselines and uncertainty. Standards help only if journals and reviewers enforce them.
- 02
Physics-informed and hybrid models
Combining learned components with known physical constraints, using zîrekiya çêkirî û fêrbûna makîneyê and high-performance, cloud and edge computing. Constraints reduce but do not remove extrapolation risk.
- 03
Uncertainty estimation
Ensembles and Bayesian methods from îhtîmal û statîstîk to flag predictions the model is unsure of. Uncertainty estimates can themselves be miscalibrated.
- 04
Open artefacts
Publishing code, data and model weights where rights allow, so others can reproduce and challenge results.
How it is studied, and what the methods cannot say.
- 01Zîrekiya çêkirî û fêrbûna makîneyêDistribution shift analysis · Fine-tuning for a specific use · Interpretability methodsApplied here
- 02Îhtîmal û statîstîkUncertainty on benchmark scores · Calibration assessment · Audit samplingApplied here
- 03Komputerkirina bi performansa bilind, ewr û kenarCompute access for public research · Energy measurement of workloadsApplied here
- 04Endezyariya nirxandin û teminata zîrekiya çêkirîAssurance cases for AI systems · Structured red teaming · Post-deployment monitoringApplied here
Where this page’s references lead.
Sazî û belgeyên ku ev rûpel li hember wan tê xwendin. Rewş û çap diguherin; her girêdan diçe ba saziya derxistinê.
Pirs û bersiv.
What is data leakage in machine learning?
Can AI replace scientific simulation?
How should AI use in research be reported?
Can AI replace experiments?
What is data leakage?
Does the platform review scientific papers?
Are AI-generated hypotheses credited?
Ji vê rûpelê delîl bidin.
rêzeya Pergalên Stratejîk is the Consortium’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.
- E-nameDergeh GW-GCRIJi vê rûpelê bişîninDelîl, rastkirin, doz, rêbaz, nirxandin, wergerandin an têketina profîlê. Bi nîşana vê rûpelê digihîje, ji ber vê yekê edîtorê xwediyê wê wê bistîne.
- ZenodoDepoya vekirîWê razînin û ji civakê re bişîninTomara xwe li Zenodo bi ORCID iD ya xwe biweşînin, paşê wê li wir ji civaka Reports re bişînin. Kurator wê vedikolin; DOI ya we dimîne.
- rêzeya Pergalên StratejîkGCRIHemû rêyên beşdariyê bibîneCureyên beşdariyê, her yek çawa tê nirxandin, tu çi navlêkirinê distînî, lîsans û tiştê ku qet nayê qebûlkirin.
Tu carî daneyên şexsî, hûrgiliyên binesaza hesas an tiştekî ku mafê we yê parvekirinê tune ne neşînin. Beşdarî berî bikaranînê tê nirxandin; şandina wê nivîskarî an mafên edîtorî nade û sponsorî tu carî encamekê nakire.
Rêbazek, danehevek an pirsek bîne.
Agahî û pirs îro vekirî ne. Pirsek referansekê distîne û gava piştî wê dide nasîn; serlêdan, drav, veqetandina hesabkirinê û tomarkirin tenê dema ku tîm, mercên wê û kapasîteya vekolînê hebin vedibin.
Belgeyên hiqûqî.
Nexşe, dane û jimarên vê malperê ji bo çarçoveyê û agahiyên giştî têne nîşandan. Ew ne şîret, ne pêşbînî û ne hişyarî ne; sînorên agahiyê di Daxuyaniyên Berpirsiyariyê û Daxuyaniyên Daneyê de hatine diyarkirin.