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Record W4387680284 · doi:10.1039/d3dd00143a

What is missing in autonomous discovery: open challenges for the community

2023· article· en· W4387680284 on OpenAlexaff
Phillip M. Maffettone, Pascal Friederich, Sterling G. Baird, Ben Blaiszik, Keith A. Brown, Stuart I. Campbell, Orion A. Cohen, Rebecca L. Davis, Ian Foster, Navid Haghmoradi, Mark Hereld, Howie Joress, Nicole Jung, Ha-Kyung Kwon, Gabriella Pizzuto, Jacob Rintamaki, Casper Steinmann, Luca Torresi, Shijing Sun

Bibliographic record

VenueDigital Discovery · 2023
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsUniversity of Manitoba
FundersBrookhaven National LaboratoryArgonne National LaboratoryBasic Energy SciencesVillum FondenBundesministerium für Bildung und ForschungOffice of ScienceLaboratory Directed Research and DevelopmentU.S. Department of Energy
KeywordsData scienceComputer science

Abstract

fetched live from OpenAlex

Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery.

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)0.046
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.954
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.070
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0090.019
Scholarly communication0.0200.054
Open science0.0060.017
Research integrity0.0130.015
Insufficient payload (model declined to judge)0.0130.005

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 head0.071
GPT teacher head0.334
Teacher spread0.264 · 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.

Study designTheoretical or conceptual
DomainMethods
GenreCommentary

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

Citations46
Published2023
Admission routes1
Has abstractyes

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