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Record W2130500236 · doi:10.1093/ilar.52.2.213

IACUC Challenges in Invertebrate Research

2011· article· en· W2130500236 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueILAR Journal · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCephalopods and Marine Biology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInvertebrateTaxonAnimal welfareBiologyEcologyVertebrate

Abstract

fetched live from OpenAlex

With billions of individuals and possibly hundreds of thousands of genera, invertebrates represent the largest number and greatest diversity of all animals used in research. Although the capacity for nociception is recognized in many invertebrate taxa, researchers and IACUC members are challenged by a lack of clear understanding of invertebrate welfare and by differing standards of moral concern for these taxa. In practice this has led IACUCs to consider invertebrates in two major groups: species worthy of increased moral concern approximating that shown to vertebrate species (this group includes cephalopods and to some extent decapod crustaceans) and all others. This dichotomy has led to differences in how invertebrate research is regulated and documented. This article presents two case studies illustrating specific concerns in invertebrate research protocols and then provides relevant information to address practical IACUC matters related to regulatory and ethical issues, sourcing and record keeping, risk management, assessment of pain and nociception in invertebrates, housing and husbandry, invasive procedures, veterinary care, and humane endpoints.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.398
GPT teacher head0.318
Teacher spread0.080 · 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