Determining diets for fishes (Actinopterygii) from a small interior British Columbia, Canada stream: a comparison of morphological and molecular approaches
Bibliographic record
Abstract
Abstract Analysis of food webs is important for defining functional components of ecosystems, but dietary data are often difficult to obtain and coarsely characterised. We compared three methods of rainbow trout (Oncorhynchus mykiss(Walbaum); Salmoniformes: Salmonidae) and prickly sculpin (Cottus asperRichardson; Scorpaeniformes: Cottidae) gut content analysis: traditional morphological taxonomy of prey items, genetic sequencing of individual prey items, and next-generation sequencing of homogenised gut contents. Prey analysis of invertebrates by morphological identification allowed order-level classifications and produced ecologically important count and mass data. Sequencing individual specimens provided greater taxonomic resolution, while next-generation sequencing of stomach contents revealed more prey diversity in the diets of both fish species as it was possible to detect prey that were degraded beyond visual recognition. Both fish species exhibited generalist feeding characteristics; however, terrestrial Insecta were a large diet component for rainbow trout. This study demonstrates an efficient approach for prey analysis using molecular techniques that complement traditional taxonomy.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".