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Record W4386888156 · doi:10.26434/chemrxiv-2023-wqp0d

Delocalized, Asynchronous, Closed-Loop Discovery of Organic Laser Emitters

2023· preprint· en· W4386888156 on OpenAlexafffund
Felix Strieth‐Kalthoff, Han Hao, Vandana Rathore, Joshua S. Derasp, Théophile Gaudin, Nicholas H. Angello, Martin Seifrid, Ekaterina Trushina, Mason Guy, Junliang Liu, Xun Tang, Masashi Mamada, Wesley Wang, Tuul Tsagaantsooj, Cyrille Lavigne, Robert Pollice, Tony Wu, Kazuhiro Hotta, L Bodø, Shangyu Li, Mohammad Haddadnia, Agnieszka Wołoś, Rafał Roszak, Cher Tian Ser, Carlota Bozal‐Ginesta, Riley J. Hickman, Jenya Vestfrid, Andrés Aguilar‐Granda, Elena L. Klimareva, Ralph C. Sigerson, Wenduan Hou, Daniel Gahler, Sławomir Lach, Adrian Warzybok, Oleg Borodin, Simon Rohrbach, Benjamín Sánchez-Lengeling, Chihaya Adachi, Bartosz A. Grzybowski, Leroy Cronin, Jason E. Hein, Martin D. Burke, Alán Aspuru‐Guzik

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsStructural Genomics ConsortiumVector InstituteUniversity of British ColumbiaUniversity of Toronto
FundersNatural Resources CanadaAdvanced Research Projects AgencyCanada First Research Excellence FundJapan Science and Technology AgencyEngineering and Physical Sciences Research CouncilWestern Canada Research GridDefense Advanced Research Projects AgencyAlliance de recherche numérique du CanadaGovernment of OntarioUniversity of TorontoCore Research for Evolutional Science and TechnologyÉcole de technologie supérieure
KeywordsWorkflowComputer scienceAsynchronous communicationCloud computingCharacterization (materials science)State (computer science)NanotechnologyMaterials scienceTelecommunicationsDatabaseOperating systemProgramming language

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.019
GPT teacher head0.268
Teacher spread0.250 · 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

Citations16
Published2023
Admission routes2
Has abstractyes

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