Can changes in length-at-age and maturation timing in Scotian Shelf haddock (Melanogrammus aeglefinus) be explained by fishing?
Bibliographic record
Abstract
Factors affecting size-at-age in fish populations include temperature and fishing where the latter can represent a strong selective force on size-at-age variation through changes in population growth and maturation. Over the past three decades, Scotian Shelf haddock ( Melanogrammus aeglefinus ) exhibited declines in maturation timing and mature fish length-at-age. Here, we examine these declines with respect to temperature, stock biomass, and fishing. We employ the thermal integral (growing degree-day, GDD, °C·day) to examine the variation in length-at-age (length-at-day, LaD, cm) and maturity (age-at-50%-maturity) that is attributable to temperature. Unexplained variation in LaD and age-at-50%-maturity remains and is characterized by declines in the LaD-at-GDD regression parameters and the thermal constant for maturity with increasing year-class. We find no significant correlation between the temperature-independent declines in LaD and stock biomass. The combination of high fishing mortality (favouring early maturation) and sustained harvesting of large fish (fast-growing, late-maturing individuals) offers the simplest explanation for the systematic decline in inferred growth and age-at-maturity for Scotian Shelf haddock. These results are consistent with other exploited populations and recent laboratory experiments quantifying the effects of fishing on size-at-age and age-at-maturity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".