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Record W4393627601 · doi:10.5281/zenodo.4477041

diatomsRcool/zebrafish_phenotype_survey: v1.0.0 Zebrafish Phenotype Survey

2021· dataset· en· W4393627601 on OpenAlexaff
Anne Thessen, John C. Achenbach, Stephan Fischer, Kimberly Hayward, Jonathan T. Hamm, Nils Klüver, Sarah Könemann, Jessica Legradi, Connor Leong, Skylar W. Marvel, John E. Mylroie, Stephanie Padilla, Dante Perone, Tony Planchart, Rafael Miñana Prieto, Celia Quevedo, David M. Reif, Kristen Ryan, Evelyn Stinkens, Lisa Truong, Colette vom Berg, Mitch Wilbanks, Bianca Yaghoobi, Melissa Haendel

Bibliographic record

VenueFigshare · 2021
Typedataset
Languageen
FieldMaterials Science
TopicDiatoms and Algae Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsZebrafishPhenotypeBiologyComputational biologyGeneticsGene

Abstract

fetched live from OpenAlex

No description provided.

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)0.006
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.168
Threshold uncertainty score0.561

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0050.003
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.1680.174

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 head0.058
GPT teacher head0.304
Teacher spread0.247 · 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
GenreDataset

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

Citations1
Published2021
Admission routes1
Has abstractyes

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