Feeding by larval fish: how taxonomy, body length, mouth size, and behaviour contribute to differences among individuals and species from a coastal ecosystem
Bibliographic record
Abstract
Abstract Data on individual stomach contents were used to describe length-dependent differences in feeding success of larvae of 11 species of fish found in coastal Newfoundland, Canada. Copepods dominated the diet with a gradual shift from nauplii to copepodites in all species. Differences in feeding success in both prey number and gut fullness among individual larvae was linked to increasing individual diet diversity in all taxa, although there was a weak decline in mean prey size. Maxilla and body length, within and among taxa, have a dominant positive influence on the potential feeding success of larval fish. In addition to differences in average stomach weight, the variability in number of prey per stomach among individuals indicates that each species perceives their prey environment in different ways. Taxonomic proximity had limited effect on differences in feeding success among taxa. The results suggest that behavioural differences among individuals and taxa, that likely reflect swimming capacity and/or prey perception/capture ability, are likely to be important elements contributing to feeding success. Body and mouth size may represent key characteristics that should be considered in evaluating differences in feeding success among species as well as among individuals within and among cohorts.
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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".