Interannual variability of diet composition and prey preference of larval redfish (Sebastes spp.) in the Gulf of St. Lawrence
Bibliographic record
Abstract
Abstract Spatiotemporal overlap between fish larvae and their planktonic prey is an important source of recruitment variability. Over the past decade, one species of redfish, Sebastes mentella, from the Gulf of St. Lawrence (GSL) produced multiple strong cohorts following decades of low recruitment, which has generated strong interest in identifying potential drivers of larval survival. The present study provides the first detailed, multi-year assessment of larval redfish (Sebastes spp.) trophodynamics. Interannual variability in larval redfish diet composition and prey selectivity was assessed using high-resolution prey identification of larval gut contents and in situ prey fields. Eggs from the calanoid copepod Calanus finmarchicus represented the most frequently consumed prey in 3 of the 4 collection years, and contributed the largest proportion of carbon ingested by redfish larvae in all years. The high consumption of C. finmarchicus eggs by larvae, combined with evidence of positive selection for this taxon in some years, supports the hypothesis of a strong trophic link between larval redfish and a key calanoid copepod in the GSL ecosystem. Our results indicate that future efforts investigating GSL redfish recruitment processes should consider environment-driven variability in the reproductive phenology and abundance of C. finmarchicus.
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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".