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Record W2762435278 · doi:10.1089/zeb.2017.1508

Report of Workshop on Euthanasia for Zebrafish—A Matter of Welfare and Science

2017· article· en· W2762435278 on OpenAlexaff
Almut Köhler, Chereen Collymore, Karin Finger‐Baier, Robert Geisler, Larissa Kaufmann, Kieran C. Pounder, Stefan Schulte‐Merker, Ana M. Valentim, Zoltán M. Varga, Jürgen Weiss, Uwe Strähle

Bibliographic record

VenueZebrafish · 2017
Typearticle
Languageen
FieldVeterinary
TopicAnimal testing and alternatives
Canadian institutionsUniversity of Toronto
FundersNational Institutes of Health
KeywordsZebrafishDirectiveAnimal welfareFish <Actinopterygii>White paperBiologyWelfareEngineering ethicsPolitical scienceEcologyComputer scienceLawGeneticsFisheryEngineeringGene

Abstract

fetched live from OpenAlex

The increasing importance of zebrafish as a biomedical model organism is reflected by the steadily growing number of publications and laboratories working with this species. Regulatory recommendations for euthanasia as issued in Directive 2010/63/EU are, however, based on experience with fish species used for food production and do not take the small size and specific physiology of zebrafish into account. Consequently, the currently recommended methods of euthanasia in the Directive 2010/63/EU are either not applicable or may interfere with research goals. An international workshop was held in Karlsruhe, Germany, March 9, 2017, to discuss and propose alternative methods for euthanasia of zebrafish. The aim was to identify methods that adequately address the physiology of zebrafish and its use as a biomedical research model, follow the principles of the 3Rs (Replacement, Reduction, and Refinement) in animal experimentation and consider animal welfare during anesthesia and euthanasia. The results of the workshop are summarized here in the form of a white paper.

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)0.020
metaresearch head score (Gemma)0.011
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0020.006
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0110.004

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 head0.166
GPT teacher head0.410
Teacher spread0.244 · 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
GenreOther

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

Citations36
Published2017
Admission routes1
Has abstractyes

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