Does the fall phytoplankton bloom control recruitment of Georges Bank haddock, Melanogrammus aeglefinus, through parental condition?
Bibliographic record
Abstract
In 2003, the Georges Bank stock of haddock ( Melanogrammus aeglefinus ) experienced the largest recruitment event recorded during its assessed history. Several hypotheses have been advanced to explain recruitment variability in this much-scrutinized stock, including variability in the retention of eggs and larvae on Georges Bank, the timing of haddock spawning, and variability in the spring bloom, which influences larval growth and survival. Although these processes may contribute to the formation of successful year classes, none of the factors associated with these previous hypotheses provides an adequate explanation of the 2003 recruitment event. We analyzed data on the dynamics of the fall phytoplankton bloom the year prior to spawning and show it to be highly correlated with subsequent recruitment. We suggest that the fall bloom affects recruitment through enhanced condition of adults and by increasing the quantity and quality of their reproductive output, which in turn leads to a higher probability of survival of their offspring. Although synoptic data on the fall bloom are limited and our analyses are correlative, our purpose is to stimulate a rigorous test of this promising “parental condition hypothesis”.
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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 | 0.000 | 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".