Bibliographic record
Abstract
Abstract A decade ago, in 2015, a paper in this journal heralded the “comeback” of Canada’s iconic Northern cod some 25 years after its collapse in the early 1990s. In this paper, the stock status determined by current and past models and recent management is reviewed, 10 years later. Summarily, surplus production and growth have stalled, some years being negative, although the stall point biomass is controversial (and well below historical norms). Structural equation models indicate the main correlate of production is spawning stock biomass (positive), whose correlates are abundances of capelin (Mallotus villosus) (positive) and harp seals (Pagophilus groenlandicus) (negative), with net production (enabling SSB growth) further reduced by fisheries. Reductions in the SSB thought necessary to avoid impairing recruitment, from 1.3 to 0.25 million t, have led to increases in harvest allocations and reopened commercial Canadian and foreign fisheries, despite advice by the Department of Fisheries and Oceans and independent science to keep removals low. Management objectives are opaque; if rebuilding, then SSB growth must remain the main objective, otherwise the Northern cod will be restrained to a shadow of its historical abundance and socio-economic and cultural importance.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".