An Evaluation of Management Objectives for Canada's Commercial Harp Seal Hunt, 1996‐1998
Bibliographic record
Abstract
Abstract: The largest existing hunt for marine mammals is Canada's commercial hunt for Northwest Atlantic harp seals ( Pagophilus groenlandicus ). From 1995 to 1998, the total allowable catch was set at a level that the Canadian Department of Fisheries and Oceans calculated would not cause the population to decline, consistent both with its stated management objectives of maintaining stable seal populations while allowing a sustainable harvest and with its stated policy of taking a precautionary approach to management. During those years, Canada's total allowable catch was progressively increased from 186,000 harp seals per year (1995) to 250,000 (1996) to 275,000 (1997 & 1998). We examined whether the government's management objectives were achieved using the conventional approach of comparing landed catches with the replacement yield estimated from a biological population model. We then conducted a second assessment, using a more modern and precautionary approach recently implemented for marine mammal management in the United States which incorporates uncertainty into management models to estimate sustainable “potential biological removal levels.” From 1996 to 1998, landed catches from Canada and Greenland exceeded Canada's estimated replacement yield. Over the same period, estimated total human‐caused mortality exceeded potential biological removal levels by 1.5 to 5.9 times. Given such levels of reported catches and estimated total human‐caused mortality, Canada's management of its harp seal hunt did not achieve its objectives. It is likely, therefore, that the population is now declining and, if recent levels of killing continue, the population will stabilize only at levels below (and possibly far below) its maximum net productivity level. Viewed from this perspective, Canada's approach to harp seal management between 1996 and 1998 cannot be deemed precautionary or risk‐averse.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".