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Record W4360876148 · doi:10.1002/bip.23497

Issue Information

2023· paratext· en· W4360876148 on OpenAlexfundno aff

Bibliographic record

VenueBiopolymers · 2023
Typeparatext
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsnot available
FundersDalian Institute of Chemical PhysicsNational Institutes of HealthChildren's Hospital of PhiladelphiaPeking UniversityUniversity of TorontoCentre National de la Recherche ScientifiqueChinese Academy of SciencesAalto-YliopistoUniversity of PennsylvaniaRice UniversityCalifornia Institute of TechnologyUniversity of Oregon
KeywordsCitationInformation retrievalWorld Wide WebComputer scienceLibrary scienceChemistry

Abstract

fetched live from OpenAlex

Correlated mutations in a protein sequence are a hallmark of coevolution and are normally interpreted as contacts in the threedimensional structure of a protein or a protein complex.However, other factors such as function, allostery or regulatory mechanisms influence the evolutionary record of protein sequences.A new method is presented that is tuned to specifically identify such long-range evolutionary couplings by taking advantage of the recent developments in machinelearning guided protein structure prediction.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.055

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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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