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Record W2750845696 · doi:10.1089/zeb.2017.1477

Breeding Zebrafish: A Review of Different Methods and a Discussion on Standardization

2017· review· en· W2750845696 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

VenueZebrafish · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsZebrafishDanioBiologyStandardizationBiotechnologyGeneticsComputer science

Abstract

fetched live from OpenAlex

In recent years, a rapidly increasing number of scientific papers have been published that utilize zebrafish (Danio rerio) as an alternative model organism in the study of a wide range of biological phenomena from cancer to behavior. This is, in large part, due to the prolific nature, relative ease of maintenance, and sufficiently high genetic homology of zebrafish to humans. With the surge of zebrafish use in animal research, the variations in methodologies of breeding and husbandry of this species have also increased. Investigators usually focus on the development and implementation of rigorous laboratory control that is specific to their studies. We suggest that the same scrutiny and attention may be required for the methods of breeding and housing of zebrafish. This article reviews a variety of zebrafish husbandry and breeding techniques and conditions employed around the world. It discusses factors ranging from numerous aspects of rearing/housing conditions through the sex ratio of the breeding group to the composition of the diet of zebrafish that may vary across laboratories. It provides some feedback on the potential pros and cons of the different methods. It argues that there is a substantial need for systematic analysis of these methods, that is, the effects of environmental factors on zebrafish health and breeding. It also discusses the question as to whether some degree of standardization of these methods is needed to enhance cross-laboratory comparability of results.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.901
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.387
Teacher spread0.324 · 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