The effects of temperature on prey-capture kinematics of the bluegill (<i>Lepomis macrochirus</i>): implications for feeding studies
Bibliographic record
Abstract
Research with ectothermic organisms has demonstrated that temperature is positively correlated with an individual's power output during locomotion. This study investigates the effect of temperature on another aspect of power output, prey-capture kinematics, of the bluegill (Lepomis macrochirus Rafinesque, 1819). Feeding sequences for two treatments of four sunfish were filmed at three temperatures (18, 24, and 30 °C) with one treatment (A) experiencing an increasing range of temperatures and the other (B) experiencing a decreasing temperature range. Directional temperatures affected prey-capture kinematics. The time required to achieve maximum lower jaw depression and maximum gape, as well as the duration of maximum gape, time to close the mouth (from the point of maximum gape), and the total bite duration, increased as water temperature decreased. In addition, both the time to maximum gape and the time to maximum lower jaw depression were longer at 18 °C for individuals in treatment A than those in treatment B. These results indicate that water temperature can bias the results of feeding studies employing kinematics that do not control for its effects as well as those that make comparisons across such studies which utilize different temperatures and taxa.
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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) | 0.001 | 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".