Feeding ecology of redfish (<scp><i>Sebastes</i></scp> sp.) inferred from the integrated use of fatty acid profiles as complementary dietary tracers to stomach content analysis
Bibliographic record
Abstract
In the northern Gulf of St. Lawrence (nGSL), redfish (Sebastes mentella and Sebastes fasciatus combined) are at record levels of abundance following the strong recruitment of three consecutive cohorts in 2011-2013 and have become by far the most abundant demersal fish in the region. Understanding redfish trophic relationships is essential for the effective management and conservation of species in the nGSL ecosystem. To date, description and quantification of redfish diet in the region have been restricted to conventional stomach content analysis (SCA). Using analysis of fatty acid (FA) profiles as complementary dietary tracers, the authors conducted multivariate analyses on 350 livers of redfish which were collected in combination with stomach contents during a bottom-trawl scientific survey in August 2017. The predator FA profiles were compared to those of eight different redfish prey types identified as dietary important with SCA. Results suggested similitude between SCA and FA results, with zooplankton prey being more related to small (<20 cm) and medium (20-30 cm) redfish (16:1n7, 20:1n?, 22:1n9 and 20:5n3) than large (≥30 cm) ones, whereas shrimp prey seemed more related to large redfish size classes (18:2n6 and 22:6n3) relative to the small and medium ones. Although the SCA offers a glimpse in the diet only based on the most recently consumed prey, analysis of FA profiles provides a mid-term view indicating pelagic zooplankton consumption on calanoid copepod and confirming high predation pressure on shrimp. This study constitutes the first attempt of combining FA with SCA to assess the diet of redfish, highlights the benefits of FA as a qualitative tool and suggests improvements for future studies.
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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) | 0.000 | 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".