Estimating dead fish quantities dropping out of gillnets when direct observations are impossible
Bibliographic record
Abstract
Abstract Fish that are caught in passive or fixed fishing gear and subsequently die and drop out of the gear, or that are removed by scavengers, do not show up in catch statistics and constitute unaccounted-for removals. Estimating these removals is challenging, particularly when direct observations are not possible because fishing conditions preclude the use of cameras or other devices or means to catch or observe the removals. Using a simple theoretical process model, we define two independent modelling approaches to estimate unaccounted mortalities resulting from drop-out. The first is based on a minimally refined analysis of commonly available fishery catch per unit effort data. The second, models data that can be obtained straightforwardly from field experiments and from at-sea fishery observers, specifically data on catch amounts, and catch composition according to three condition categories – live, dead-fresh and degraded. We apply these modelling approaches to data from the Gulf of St. Lawrence (Canada) Greenland halibut ( Reinhardtius hippoglossoides ) gillnet fishery, for which multi-day soak durations have long been suspected of generating significant unaccounted fish death while only little evidence is available. In fact, observed proportions of dead decaying and ultimately discarded catch for this fishery are small and increase only a little with increased soak duration, constituting seemingly contradictory evidence. The two modelling approaches produce similar results, estimating that total dead catches of Greenland halibut were on average 4.7 to 5.4 times the recorded landings over the period from 2000 to 2024. This result is consistent with the high mortality levels suggested by the assessment for this stock. Of broader relevance to other gillnet fisheries worldwide that may employ shorter soak duration, we found that estimated unobserved catch losses equalled retained catch after soak durations of 15 hours or less. Importantly the condition of fish in catches may belie the quantities of dead fish losses. Failing to account for fishing derived loss of the magnitude estimated here could result in important biases in stock assessments and associated sustainable harvesting frameworks, in addition to constituting an important source of waste.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".