Individual growth history of larval Atlantic mackerel is reflected in daily condition indices
Bibliographic record
Abstract
Abstract We tested the hypothesis that faster-growing Atlantic mackerel (Scomber scombrus) larvae generally achieve better feeding success than their slower-growing counterparts. Feeding success and growth were derived from the analysis of gut content and otolith microstructure of larvae from four cohorts (1997–2000) from the southern Gulf of St Lawrence. We observed a high degree of serial correlation in otolith growth (OG) from hatching, suggesting that events occurring early in life have long-standing effects on future growth potential. The diet of fast-growing individuals was dominated by large prey, such as cladocerans and fish larvae (including conspecifics), while slow-growing larvae foraged primarily on smaller copepod naupliar stages. Both feeding success (stomach content) and an index of condition (body depth) were positively correlated with OG, and these relationships explained approximately three times more variance in mackerel than in larval radiated shanny (Ulvaria subbifurcata) of similar size. Relationships linking age-dependent scores of body depth to feeding success and growth were ∼3.5–4 times stronger than those based on length-dependent indices, suggesting that differences in energy allocation during early ontogeny may play a significant role in determining an individual's capacity to cope with variations in feeding conditions.
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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".