MétaCan
Menu
Back to cohort
Record W2890670939 · doi:10.1080/19396368.2018.1511764

Protein evolution revisited

2018· review· en· W2890670939 on OpenAlexafffund
Peter L. Davies, Laurie A. Graham

Bibliographic record

VenueSystems Biology in Reproductive Medicine · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsQueen's University
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsBiologyEvolutionary biologyComputational biologyGenetics

Abstract

fetched live from OpenAlex

Antifreeze proteins (AFPs) protect marine fishes from freezing in icy seawater. They evolved relatively recently, most likely in response to the formation of sea ice and Cenozoic glaciations that occurred less than 50 million years ago, following a greenhouse Earth event. Based on their diversity, AFPs have independently evolved on many occasions to serve the same function, with some remarkable examples of convergent evolution at the structural level, and even instances of lateral gene transfer. For some AFPs, the progenitor gene is recognizable. The intense selection pressure exerted by icy seawater, which can rapidly kill unprotected fish, has led to massive AFP gene amplification, as well as some partial gene duplications that have increased the size and activity of the antifreeze. The many protein evolutionary processes described in Gordon H. Dixon's Essays in Biochemistry article will be illustrated here by examples from studies on AFPs. Abbreviations: AFGP: antifreeze glycoproteins; AFP: antifreeze proteins; GHD: Gordon H. Dixon; SAS: sialic acid synthase; TH: thermal hysteresis.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0020.006
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.003

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 head0.043
GPT teacher head0.335
Teacher spread0.292 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueSystems Biology in Reproductive MedicineSame topicGenomics and Phylogenetic StudiesFrench-language works237,207