Re-evaluating Atlantic cod mortality including lobster bycatch: where could we be today?
Bibliographic record
Abstract
Full accounting of fisheries mortality is one of the most tractable ways to improve stock assessments. However, it can be challenging to obtain in cases when missing catch comes from small-scale nontarget fisheries unrequired to report incidental catch. Atlantic cod (Gadus morhua) in the Gulf of Maine (GoM), USA, once served as a regionally important fishery, but has been serially depleted to <5% of historic spawning stock biomass. Recent management efforts to rebuild GoM cod have largely failed. We test the hypothesis that unaccounted bycatch of Atlantic cod in the Maine American lobster (Homarus americanus) fishery is a substantial missing piece in the GoM Atlantic cod assessment. We integrated multiple scenarios of hind-casted discards into the two accepted regional cod assessment models from 1982 to 2016. Incorporation of discards improved the assessment bias for both models (10%–15%), increased estimates of spawning stock biomass (4%), and decreased estimates of fishing mortality (9%). A novel evaluation of longitudinal model bias suggests that alternative modelling approaches or specifications may be warranted. We highlight the importance of accounting for all fishery-related mortality and the need for methods to deliver more comprehensive estimates from both target and nontarget fisheries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".