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Record W1980347175 · doi:10.3138/jvme.38.2.150

Literature Search Strategies to Comply with Institutional Animal Care and Use Committee Review Requirements

2011· article· en· W1980347175 on OpenAlexvenueno aff
Daureen Nesdill, Kristina M. Adams

Bibliographic record

VenueJournal of Veterinary Medical Education · 2011
Typearticle
Languageen
FieldVeterinary
TopicAnimal testing and alternatives
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal welfarePrincipal (computer security)DistressAnimal testingOrder (exchange)PsychologyRisk analysis (engineering)BusinessComputer scienceClinical psychology

Abstract

fetched live from OpenAlex

Under the US Animal Welfare Act, principal investigators who propose to use animals in their research must demonstrate that they have considered alternatives to potentially painful or distressful procedures when submitting applications to Institutional Animal Care and Use Committees (IACUCs). IACUCs requires that applicants conduct a current literature search to determine if alternatives are available to substitute the proposed animal use and, if the proposed study involves pain or distress, that more humane procedures, as they are described in the literature, be considered. This paper suggests literature search strategies that can be considered for use in order to comply with this IACUC requirement.

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)0.252
metaresearch head score (Gemma)0.589
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.748
Threshold uncertainty score0.922

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2520.589
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0140.008
Bibliometrics0.0620.043
Science and technology studies0.0050.005
Scholarly communication0.0090.007
Open science0.0080.007
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0880.020

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 head0.396
GPT teacher head0.481
Teacher spread0.085 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
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

Citations8
Published2011
Admission routes1
Has abstractyes

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