Bibliographic record
Abstract
The Canadian Council on Animal Care (CCAC) publishes data on over 25 species of animals used in science, and the US Department of Agriculture publishes data on six of those species. Between 1980 and 1999, the reduction in animal use was found to be correlated between Canada and the USA for dogs (r = 0.944, p < 0.001), cats (r = 0.839, p < 0.001), rabbits (r = 0.852, p < 0.001) and hamsters (r = 0.716, p < 0.01), with no significant correlation found for non-human primates and guinea-pigs. On the basis of the four species where correlation between the two countries was found for reduction in use, the mean ratio of the number of animals used in the USA compared to the number used in Canada was 17.0 +/- 7.5. The CCAC data for these six US-regulated species were used in an analysis of regression with multiple predictors to test whether they could be used to predict the total number of animals used. No significant correlation was found. However, using the same analysis, rats, mice, fish and birds were found to be highly correlated with the total number of animals used (r2 = 0.9835, p < 0.005). The regression equation developed by using Canadian data was validated using UK animal use numbers. An almost perfect fit between the estimated values provided the evidence that total animal use in Canada and the UK decreased at about the same pace during the 1990s. Animal use data can be a useful tool to monitor the implementation of reduction measures. However, their use for the monitoring of refinement measures requires care and analysis. For example, the sustained downward trend in the number of experiments causing severe pain in unanaesthetised animals (category of invasiveness [CI] E) observed in Canada and the USA between 1996 and 1999 is indicative of effective refinement, but it would be misleading to interpret the increase in the number of animals used in Canada under CI D in 1997 as an indication of greater pain and distress. In fact, the larger number of animals in CI D resulted at least in part from the implementation of new CCAC guidelines designed to ensure better monitoring of transgenic animal care and use.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".