A potential model system for studying the genetics of domestication: behavioral variation among wild and domesticated strains of zebra danio (<i>Danio rerio</i>)
Bibliographic record
Abstract
The process of domestication in fish is fundamentally important to conservation efforts because of the extensive use of hatcheries to mitigate population declines. Research into the genetic changes associated with the domestication process in many endangered species is impeded by a lack of genomic tools, long generation times, and large space requirements. The study of the genetics of fish domestication could therefore benefit from the introduction of a model system. In this paper, we document behavioral and growth rate differences observed between a domesticated laboratory strain of zebra danio (Danio rerio) and a strain newly introduced into the laboratory from its native habitat in India. Domesticated zebra danio showed a higher degree of surface orientation, a reduced startle response, and higher growth rate compared with wild zebra danio. Wilddomesticated interstrain hybrids were intermediate in phenotype for all traits. When strains were reared together, most interstrain behavioral differences were maintained, indicating a genetic basis underlying the interstrain phenotypic variation. Phenotypic differences observed in this study are consistent with the effects of domestication in other fish species, indicating that the zebra danio can be used as a model system for studying the genetics of the domestication process in fish.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".