Causes of mortality in depleted populations of Atlantic cod estimated from multi-event modelling of mark–recapture and recovery data
Bibliographic record
Abstract
Knowledge on mortality causes is key for an effective management of animal populations and can help to restore depleted fish stocks. Here we investigated the mortality dynamics of coastal Atlantic cod (Gadus morhua) in Skagerrak, southern Norway, by analyzing local mark–recapture and recovery data collected from 2005 to 2013 (N = 9360 fish, mean length = 41 cm, range = 16–93 cm). By applying multi-event models to the data, we could link field observations to multiple “dead states” and estimate the proportion of deaths associated with different fishing gears while controlling for unobserved mortality and detection errors. Deaths due to hand lines and fixed gear types were dominant compared with other causes, especially in legal-sized cod (≥40 cm). Gear-specific mortality changed over time and between size classes, but annual survival remained low and stable (∼0.3). Assuming fully additive mortality, we predicted annual survival of cod to be above 0.5 if only one or both of the dominant gear types were removed, providing insights on the relative impact of diverse harvesting practices on local population dynamics.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".