Extent and possible conservation implications of fish use for research, testing and education in North America
Bibliographic record
Abstract
1. Fish species are used globally for fundamental research, product and environmental testing, and education, but this use has not hitherto been documented. The number of fish used for research and education in North America currently represents approximately one-quarter of all animal use for these purposes; only mice are used in higher numbers. Data from the Canadian Council on Animal Care, various animal care committees from Canadian universities, and the United States Fish and Wildlife Service are collated and summarized. 2. In Canada nearly a half a million fish were used for research and education each year from 1996 to 2001. We estimated that the United States used over four times that amount for the same purposes, totalling approximately 2.8 million fish each year. 3. At least 100 fish species, representing 27 taxonomic orders, were used for research and education in North America from 1996 to 2001. Close to one-third of the species had life-history characteristics that might make them particularly susceptible to over-exploitation. This potential vulnerability, coupled with data from animal care facilities in Canada suggesting that nearly half of all fish used for research and education were wild-caught, should prompt further documentation of such fish uses in order to assess their sustainability. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".