Foraging by larval fish: a full stomach is indicative of high performance but random encounters with prey are also important
Bibliographic record
Abstract
Abstract This study contrasts diet composition patterns of larval fish categorized as strong and weak foragers, identified from quadratic relationships between larval length and the number of prey eaten, for 11 fish species. Two sets of alternative hypotheses test whether strong foragers (1) exhibit precocious behaviour by eating later developmental stages of copepods, and (2) take advantage of random encounters with zooplankton, based on the contrast between the two categories in each 1 mm length-class. Results indicate that strong foragers shift their feeding toward earlier copepod developmental stages, which was most apparent in four flatfish species, and demonstrate stronger overall prey selectivity than weak foragers. Inverse modeling revealed the latter is achieved through increases in apparent prey perception and/or responsiveness to dominant prey types (i.e. nauplii and copepodites) and declines for less frequent prey (e.g. veliger and Cladocera). Foraging strength increased modestly with larger eye diameter and mouth gape. Two possible explanations for prey selection patterns are that strong foragers have inherently different capacity to perceive and attack prey, or that after initially eating sufficient large prey to meet metabolic requirements fuller stomachs depend on the ability of larval fish to take advantage of random encounters.
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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 | 0.000 | 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) | 0.003 | 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".