MétaCan
Menu
Back to cohort
Record W4285141178 · doi:10.1039/d2dd00029f

A self-driving laboratory designed to accelerate the discovery of adhesive materials

2022· article· en· W4285141178 on OpenAlexafffund
Michael B. Rooney, Benjamin P. MacLeod, Ryan Oldford, Zachary J. Thompson, Kolby L. White, Justin Tungjunyatham, Brian J. Stankiewicz, Curtis P. Berlinguette

Bibliographic record

VenueDigital Discovery · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsMaRS InnovationCanadian Institute for Advanced ResearchUniversity of British Columbia
FundersCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaCMC MicrosystemsCanada First Research Excellence FundCanada Research ChairsCanadian Institute for Advanced Research3M
KeywordsAdhesiveSelf adhesiveMaterials scienceRobotNanotechnologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This self-driving laboratory combines a robot for preparing and testing adhesive bonds with an optimizer to rapidly improve adhesive formulations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.238
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations50
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueDigital DiscoverySame topicMachine Learning in Materials ScienceFrench-language works237,207