Estimation Of The Exploitation Rates And Migration Rates For Cod (Gadus Morhua) In Nafo Divisions 3kl And Subdivision 3ps During 1997-2000 From Tagging Experiments
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.It is possible to estimate the exploitation rate (fraction of stock removed) by a Þshery using data from Þsh tagging experiments. Exploitation rate estimates can then be used in conjunction with estimates of landings by the Þshery to estimate stock size. This is particularly useful for inshore stock components if the near-shore topography causes problems for common survey methods, e.g. trawl and acoustic surveys. A series of comprehensive Atlantic cod tagging studies have been conducted in NAFO Divisions 3L and 3K (3KL) and Subdivision 3Ps during 1997-2000. Over 49 000 cod in total have been tagged and released in 123 experiments. We use 7394 tag-returns from the cod Þsheries in 1997-2000 to estimate the exploitation and migration rates of cod in these regions. Migrations of tagged Þsh must be accounted for to get unbiased estimates of exploitation rates. In addition, knowledge of migration rates is also required to assess the total exploitation rates on stock components. We use a model that incorporates length-based exploitation rates for different gear types. The model also incorporates estimates of tagging mortality rates, tag loss rates, tag reporting rates, and Þsh growth rates. The exploitation rate estimates are used with reported landings by the Þshery to estimate weekly cod biomass and abundance in six geographic zones around the coast of Newfoundland. The difficulties caused by stock migrations will be illustrated and discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".