Atlantic Cod Growth History in Flemish Cap Between 1981 and 2016: The Impact of Fishing and Climate on Growth Performance
Bibliographic record
Abstract
Flemish Cap cod collapsed in the early 90’s after facing multiple threats due to climate variability and anthropogenic pressures. A master 35-year cod growth chronology was reconstructed in the present study by using sclerochronology techniques. Three additional chronologies were developed to account for the age-dependent effect of maturity and fishing intensity on growth. All chronologies showed that cod growth has decreased over the years and it is currently at the minimum level in the historic of data series. Hierarchical mixed-effects models were applied to the master chronology to identify extrinsic drivers of growth. The master chronology was divided into 3 periods: Premoratorium (1988-1998), Moratorium (1999-2009), and Postmoratorium (2010-2019). Our results showed that drivers of cod growth in the Flemish Cap varied between periods (Premoratorium: bottom temperature and fishing mortality, Moratorium: cod and pandalus abundance, and Postmoratorium: cod abundance and thickness of the cold intermediate layer). Our results suggest that density-dependence processes, food availability, and global warming may be behind the lack of recovery of the Flemish Cap cod stock. The Flemish Cap cod growth chronology shows variations on growth over time and identifies its drivers, having the potential to be included in the NAFO stock assessments or to be used to set dynamic reference points that guarantees the stock sustainability.
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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 | 0.001 | 0.001 |
| 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".