Modeling feeding processes: a test of a new model for sea bream (<i>Sparus aurata</i>) larvae
Bibliographic record
Abstract
An organism's feeding rate is governed by constraints imposed by processes associated with consumption. We present a general feeding model that incorporates encounter, successful pursuit, handling, and digestion in one functional representation where we treat digestion as a parallel process. The model produces type II functional response curves. However, the asymptotic maximum feeding rate is determined by the sum of the time spent for handling and digesting a prey minus the gain in time, since the digestion process is parallel to the handling process. We use our model in combination with existing models of encounter, successful pursuit, and digestion to evaluate the feeding rate of fish larvae. We test the model against experimental data for sea bream (Sparus aurata) larvae and find a very close quantitative correspondence between predictions and experiments. Sensitivity analysis shows that for the early developmental stages, the model is sensitive to parameters related to the visual and locomotion abilities of larvae to detect and capture the prey. Later, when they establish these abilities, the choice of accepting or not the prey becomes more important.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".