Do environmental factors affect recruits per spawner anomalies of New England groundfish?
Bibliographic record
Abstract
Abstract We evaluated the influence of environmental factors on recruits per spawner (RS) anomalies of 12 New England groundfish stocks. Nonparametric methods were used to analyse time-series of RS anomalies derived from stock-recruitment data in recent assessments. The 12 stocks occur in three geographic regions: the Gulf of Maine (cod Gadus morhua, redfish Sebastes fasciatus, winter flounder Pseudopleuronectes americanus, American plaice Hippoglossoides platessoides, witch flounder Glyptocephalus cynoglossus, and yellowtail flounder Limanda ferruginea), Georges Bank (cod, haddock Melanogrammus aeglefinus, and yellowtail flounder), and Southern New England (summer flounder Paralichthys dentatus, yellowtail flounder, and winter flounder). Randomization tests were applied to detect years when RS anomalies were unusually high or low for comparison with oceanographic conditions such as the 1998 intrusion of Labrador Subarctic Slope water into the Gulf of Maine region. Randomization methods were also used to evaluate the central tendency and dispersion of all RS anomalies across stocks. Average RS anomalies were significantly positive in 1987 across stocks and regions, indicating that environmental forcing was coherent and exceptional in that year. Responses of RS values of individual stocks to lagged and contemporaneous environmental variables such as the North Atlantic Oscillation (NAO) index, water temperature, windstress, and shelf water volume anomalies were evaluated using generalized additive models. Overall, the NAO forward-lagged by 2 years had the largest impact on RS anomalies. This apparent effect is notable because it could provide a leading indicator of RS anomalies for some commercially exploited stocks. In particular, the three primary groundfish stocks on Georges Bank (cod, haddock, and yellowtail flounder) all exhibited positive RS anomalies when the NAO2 variable was positive.
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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 | 0.002 |
| 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".