Environmental Stochasticity And Recruitment Anomalies Of Gadoids In The North Atlantic
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.It has proven difficult to find and describe stock spawning biomass and recruitment relationships in spatially complex and large-scale ecosystems in general, even though they exist in theory. To elucidate stock recruitment relationships, it has been argued that such models must include environmental factors and lately, there have been an increasing number of studies suggesting relationships between large-scale physical processes and year class strength of different fish stocks. Then again, exploring environment-recruitment correlations should include stock spawning biomass since many trends in recruitment are a result of changes in stock spawning biomass. In this study, we used a synthetic approach to explore the relationship between recruitment success of gadoids and the North Atlantic Oscillation, NAO. The analysis included data for cod (Gadus morhua), haddock (Melanogramus aeglefinus) and saithe (Pollachius virens) stocks in the North Atlantic. NAO is related to the direction and strength of the wind, temperature and precipitation over the North Atlantic and consequently reflects shifts in general climate conditions. Overall, our results suggest that: recruitment success in these gadoid fish is related to area; North sea and Newfoundland having the highest relative recruitment success whereas southern and northern areas have relatively lower recruitment success. This geographical pattern in recruitment success can manly be related to temperature. Furthermore, recruitment anomalies, deviations in recruitment not explained by stock biomass, were correlated to NAO. The relationship between recruitment anomalies and NAO has different functional forms, which suggests that different properties of NAO, i.e. temperature, wind direction, currents, might shape the level of recruitment success.
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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) | 0.000 | 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".