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Record W2973818170 · doi:10.1002/0471142301.nsa04es27

Animal Identification

2004· article· en· W2973818170 on OpenAlexaff
John Donovan, Patricia Brown

Bibliographic record

VenueCurrent Protocols in Neuroscience · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsIdentification (biology)Computational biologyBiologyComputer scienceEcology

Abstract

fetched live from OpenAlex

Proper identification of animals is crucial in research, whether the animals are being used as experimental subjects, for breeding purposes, or as a source for tissues, cells, or fluids. This unit provides specific protocols for the ear punch (notch) and ear tag methods for mouse, rat and hamster; tattoos for mouse, rat and rabbit; and the correct use of a subcutaneous transponder system for all of the above species.

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)0.003
metaresearch head score (Gemma)0.004
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: Methods · Consensus signal: Methods
Teacher disagreement score0.075
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.002
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0030.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0750.069

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 head0.069
GPT teacher head0.393
Teacher spread0.325 · 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
GenreMethods

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

Citations5
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Protocols in NeuroscienceSame topicIdentification and Quantification in FoodFrench-language works237,207