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Record W6950312001 · doi:10.5683/sp3/wudmnu

Attitudes of laboratory animal professionals and researchers towards carbon dioxide euthanasia for rodents and perceived barriers to change

2022· dataset· en· W6950312001 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2022
Typedataset
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsQualitative researchAmbivalenceScale (ratio)CompromiseQualitative analysisQualitative propertyResearch ethicsAnimal welfare

Abstract

fetched live from OpenAlex

In this study we conducted an online survey of Canadian and European laboratory animal professionals and researchers (n 592) to assess their attitudes towards the use of CO2 and alternative methods for rodent euthanasia using quantitative 7-point scale (from 1 ( strongly oppose) to 7 ( strongly favour) and qualitative (open-ended text) responses. CO2 was identified as the most common method used to kill rodents, and attitudes towards this method were variable and on average ambivalent (mean SD score on our 7-point scale was 4.4 1.46). Qualitative analysis revealed four themes relating to participant attitude: (a) the animal’s experience during gas exposure; (b) practical considerations for humans; (c) compromise between the animal’s experience and practical considerations; and (d) technical description of the procedure or policies. Many participants (51%) felt that there were alternatives available that could be considered an improvement over CO2, but perceived barriers to implementing these refinements. Qualitative analysis of these responses revealed five themes: (a) financial constraints; (b) institutional culture; (c) regulatory constraints; (d) research constraints; and (e) safety concerns. In conclusion, concerns regarding the use of CO2 often focused on the animal’s experience, but barriers to alternatives related to operational limitations. New research is now required on to how best to overcome these barriers.

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 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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.495
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.058
GPT teacher head0.344
Teacher spread0.286 · 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 teacher head, not a consensus.

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

Citations0
Published2022
Admission routes2
Has abstractyes

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