Discard Mortality of Sea Scallops <i>Placopecten magellanicus</i> Following Capture and Handling in the U.S. Dredge Fishery
Bibliographic record
Abstract
Abstract Discard mortality can represent a potentially significant source of uncertainty for both stock assessments and fishery management measures. While the family Pectinidae is considered to be robust to the capture and handling process, understanding species-specific discard mortality rates is critical to characterize both population dynamics and to develop regulatory measures to meet management objectives. The discard mortality rate for the U.S. dredge fishery of sea scallop Placopecten magellanicus was estimated empirically via a retention study aboard industry vessels under commercial conditions. Over 16,000 sea scallops were assessed via a composite index of scallop vitality that consisted of semiqualitative measures of both overt trauma (shell damage) and response to stimuli. Results indicate that overall sea scallop discard mortality was 21% and consistent with the values currently assumed in the stock assessment. Survival mixture models support the utility of a simple metric of physical trauma as an effective predictor of mortality. Exposure time was also identified as a positively correlated factor that was important in describing the discard mortality process. Application of experimental results highlight the need to consider some operational characteristics of the fishery to reduce potential discard mortality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".