Congruence of stock production and assessment areas? An historical perspective on Canada’s iconic Northern cod
Bibliographic record
Abstract
Fisheries management requires spatially congruent production and assessment areas. Canada’s Northern cod (Gadus morhua), initially considered a stock complex distributed from northern Labrador to the northern Grand Bank, had its northern boundary reduced to southern Labrador in the early 1970s. Spatial incongruence has resulted in spawning stock biomass (SSB) and recruitment (R) between historical and recent eras. To investigate temporal changes, four stanzas of SSB and R were derived from statistical Perron breaks. In stanza 1, the 1960s, spawning off northern Labrador coincided with higher SSB and R than in following stanzas from the 1970s onward. SSB–R relationships that include 1960s data do not represent potential production from a more southerly distributed stock. Loglinear models of R (density-dependent models did not improve fit) indicated SSB had greatest effect and, with indices of climate and south–north distribution, explained 86% of variance. Lack of density dependence suggests long-standing recruitment overfishing, making reference points problematic. SSB growth is suggested as an alternative management target. Rebuilding the Northern cod to historical abundance requires a full “portfolio” of spawning from northern Labrador to the Grand Bank (a remanaged 2+3KL stock). With a contracted range, lower production should be anticipated.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".