Addressing the unique challenges of community-based capture–hold–release aquariums through a facility health program
Bibliographic record
Abstract
Community-based capture–hold–release (CHR) aquariums were developed to (i) increase community connection to local marine environments by displaying local animals, (ii) avoid negative perceptions about holding animals by minimizing the time any individual is held captive, and (iii) operate with a low ecological footprint. CHR aquariums in British Columbia, Canada, require government-issued licences and permits to capture, hold, and release animals, a condition of which is that neither capture nor release can result in negative ecological, genetic, or disease impacts on wild populations in the collecting or receiving waters. Growth in the popularity of CHR aquariums is placing them under greater scrutiny from permitting agencies. Because of variability between facilities and a lack of performance standards, CHR aquariums cannot be assured of a consistent assessment. This paper proposes a CHR Aquarium Health Program that transparently and consistently provides assurances that they are socially and ecologically safe and recognizes the unique challenges of small-scaled, often rural aquariums. The value of this approach is discussed with respect to 10 years of implementation at the Ucluelet Aquarium.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".