Missing the safety net: evidence for inconsistent and insufficient management of at-risk marine fishes in Canada
Bibliographic record
Abstract
Marine conservation is often perceived as being in conflict with fisheries management. In Canada, at-risk marine fishes denied listing under the Species at Risk Act (SARA) are meant to receive comparable measures under the Fisheries Act. We assess the effectiveness of these Acts by examining (i) how long it takes a marine fish assessed as being at risk to move through the process and receive conservation measures, (ii) whether there are biases against marine fishes in the SARA process additional to the known listing bias, and (iii) when denied listing, to what extent these species are protected by the Fisheries Act. Overall, at-risk marine fishes typically spend 3.25 years under consideration for SARA, during which time they receive no additional protection. Endangered and Threatened marine fishes (i.e., those most at risk) face the greatest bias and receive the least protection; their SARA decisions are typically delayed, with almost 5 years usually passing between their COSEWIC (Committee on the Status of Endangered Wildlife in Canada) assessment and listing decision; most (70.6%) are then denied listing, after which the Fisheries Act provides few of the SARA-required measures. For SARA-listed marine fishes, recovery strategies are usually late and to date no action plans have been produced. Marine fish conservation is hindered by SARA’s slow pace, incomplete recovery measures, and inadequate implementation of the Fisheries Act. We provide recommendations for improving conservation of at-risk marine fishes in Canada.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".