Model Evaluation of Linear Gut Evacuation in the Larval Radiated Shanny Using a Combination of Laboratory and Field Data
Bibliographic record
Abstract
Abstract The combination of gut contents and evacuation rate is an important tool with which to determine the in situ feeding rates of many organisms. Traditionally used equations of gut evacuation models, however, have made comparisons among models difficult. For consistency, we changed the notation of the linear gut evacuation model to provide a rate‐constant parameter (r) similar to the one commonly used in the exponential model. In a previous analysis using examples from the literature, we demonstrated that prolonged retention of food after a true linear pattern of gut evacuation may be mistakenly identified as an exponential pattern. We explored the linear gut evacuation rate model empirically and in more detail using the radiated shanny Ulvaria subbifurcata as the model species. Rate constants based on field data were similar to those determined in the laboratory. The basic assumption of nonfeeding during darkness was met, indicating that foraging was primarily based on visual cues. As predicted by our model, both laboratory and field data confirmed that gut evacuation was linear and characterized by a rate constant (i.e., instantaneous rate of gut evacuation) that was independent of initial gut fullness. Rate constants were, however, influenced by fish size and in some cases by the size of food particles. Analysis of triplicate time courses of gut evacuation in the field showed that larger larvae evacuated food at a relatively higher rate than did smaller larvae.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".