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Record W4286471126 · doi:10.1017/s1431927622005074

3D Flexible Refinement: Structure and Motion of Flexible Proteins from Cryo-EM

2022· article· en· W4286471126 on OpenAlexaff
Ali Punjani, David J. Fleet

Bibliographic record

VenueMicroscopy and Microanalysis · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsVector InstituteUniversity of Toronto
Fundersnot available
KeywordsVolume (thermodynamics)Library scienceMotion (physics)Computer sciencePhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Ali Punjani, David Fleet; 3D Flexible Refinement: Structure and Motion of Flexible Proteins from Cryo-EM, Microscopy and Microanalysis, Volume 28, Issue S1

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)0.004
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 score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0100.003
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0230.007

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 head0.006
GPT teacher head0.277
Teacher spread0.271 · 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

Citations37
Published2022
Admission routes1
Has abstractyes

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