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Record W2406048443 · doi:10.1177/026119290403201s89

Incorporating Animal Alternatives in a Training Programme in Laboratory Animal Care and Use

2004· article· en· W2406048443 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

VenueAlternatives to Laboratory Animals · 2004
Typearticle
Languageen
FieldVeterinary
TopicAnimal testing and alternatives
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsTraining (meteorology)Animal welfareSession (web analytics)Medical educationMedicinePsychologyComputer science

Abstract

fetched live from OpenAlex

Through the years, scientists and technicians, within their laboratories, trained individuals new to animal research. In the mid-1980s, new and revised laws/guidelines for animal research, including training, were instituted. The process of training scientists, technicians and students is recognised as essential for animal welfare and good science. Under the current and future revisions of the mandates, training must include specific topics. Training must include details of various regulations and guidelines, proper handling and care for animals, proper pre-procedural and post-procedural care of animals, aseptic surgical methods and procedures, proper use of anaesthetics, analgesics and tranquilisers, methods whereby deficiencies in animal care and treatment are reported, and a search for alternatives to the use of animals. Training individuals in clinical techniques (injection, blood collection, anaesthesia, etc.) is critical for humane treatment of animals, safety for the trainee and reliable animal data for the scientific project. Written materials about the clinical techniques should be provided at the training session, and training records must be kept.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.138
GPT teacher head0.380
Teacher spread0.242 · 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