Revealing the relationship between feeding and growth of larval redfish (<i>Sebastes</i> sp.) in the Gulf of St. Lawrence
Bibliographic record
Abstract
Abstract Year-class strength of fish stocks is often set during the larval stage, with fast growth being a favourable factor leading to strong recruitment. Following 30 years of poor recruitment, redfish (Sebastes sp.) in the Gulf of St. Lawrence (GSL) produced unprecedentedly strong year classes in 2011–2013. The relationship between larval diet and growth that potentially drove these successful recruitment events is unknown. Gut content and otolith microstructure of redfish larvae collected from the GSL in 1999 and 2000 were analysed in order to understand the relationship between recent feeding success and growth in larval redfish, and identify feeding behaviours that are associated with fast growth. Growth was positively correlated with larval body depth (BD), which was used as a morphological proxy for growth. Prey type and size were the best explanatory variables of larval BD after obligatory first-feeding. Larvae that consumed large naupliar stages of frequently consumed copepod taxa were deeper-bodied and grew more quickly than larvae that consumed redfish's preferred prey, Calanus finmarchicus eggs. Warming GSL waters have shifted the phenology of commonly consumed prey taxa earlier in the season, which may increase the overlap between redfish and naupliar prey that drive fast growth, survival, and potentially 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 | 0.000 |
| 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".