Extending the Northern Cod (Gadus morhua) assessment model - part I : bridging gaps with additional data and model variations
Bibliographic record
Abstract
Since 2016, the Northwest Atlantic Fisheries Organization (NAFO) Division (Divs.) 2J3KL (or Northern) cod stock has been assessed with a complex integrated state-space population model called NCAM (Northern Cod Assessment Model). While the time series in base-case NCAM begins in 1983 – when the Research Vessel (RV) survey started covering NAFO Divs. 2J, 3K, and 3L. Older data used in previous assessment models for Northern cod, such as reported landings and catch-at-age, have gone unused. Though limited to selected years, there is also considerable historical data from the tagging program that has yet to be utilized in a formal assessment model. Finally, data from two juvenile cod monitoring programs have yet to be utilized in an assessment model for Northern cod. Here we document the process and assumptions behind the inclusion of additional reported landings, catch-at-age, tagging data, and juvenile survey data into NCAM. Much of these data extend back to 1954. Additionally stock-recruitment (S-R) relationships are explored as are alternate approaches to estimating baseline rates of natural mortality (M; e.g., allometric M). The application of additional data and model variations not only extends the temporal perspective on Northern cod stock trends, but also enhances our understanding of critical aspects such as recruitment, the S-R relationship, and the estimation of M. These extensions bridge gaps in our knowledge, enable the reassessment of reference points, and offer a more robust foundation for informed fisheries management decisions.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".