Peer-reviewed scientific contributions from Canadian zoos and aquariums
Bibliographic record
Abstract
With the widespread loss of biodiversity, zoos and aquariums are striving to become leaders in biodiversity conservation and research. Canada’s Accredited Zoos and Aquariums (CAZA) is a nonprofit organization created to represent its members, including as agencies of conservation and science. However, the contribution of CAZA members to conservation and science has not been quantified. We used research productivity in the form of peer-reviewed publications to systematically quantify biodiversity conservation engagement by CAZA institutions. We extracted publications from the ISI Web of Science database and found that the annual number of publications increased over time. CAZA members published most in the area of veterinary science, with few publications in biodiversity conservation. Organization age, research-orientated mission statements, and financial assets were significant predictors of research productivity. CAZA institutions also published significantly less ([Formula: see text] = 12.5 ± 5.52 SE) than members of the Association of Zoos and Aquariums ([Formula: see text] = 24.27 ± 5.08 SE), based in the United States. Zoos and aquariums are important resources in mitigating biodiversity loss, and are increasing their research output in this area. Nonetheless, only a small proportion of publications were in biodiversity conservation, and the majority of all publications occurred in zoo-centric journals.
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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 | 0.022 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.052 | 0.073 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".