Year-class detection reveals climatic modulation of settlement strength in the European lobster, <i>Homarus gammarus</i>
Bibliographic record
Abstract
Understanding the nature of recruitment relationships in the European lobster, Homarus gammarus, has been an intractable problem because of difficulties associated with quantification of its scarce planktonic larvae and early benthic phase. We attempt to address this problem by analyzing the age composition of a population off the northeast coast of England. Age-dependent in situ deposits of neurolipofuscin in the eyestalk are used as an age index. An approach is presented that accounts and (or) corrects for the two most important potential sources of error in age determinations by this technique, namely environmental temperature variation and unexplained individual variation. This yields, for the first time in very long-lived clawed lobsters, reproducible catch age structures with year-class resolution. The method should be generally applicable to crustaceans. Cross-correlation analysis shows that larval settlement strength in the European lobster is associated with local sea temperatures and onshore winds in a manner similar to that reported for other lobsters. These findings have important implications for stock assessment, particularly the use of traditional models dependent on size and steady state, yield forecasting, the effects of global climate change, arguments about spawner protection or restocking, and the spawnerrecruit relationship.
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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 | 0.000 |
| 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".