The cost of capturing prey: measuring largemouth bass (<i>Micropterus salmoides</i>) foraging activity using glycolytic enzymes (lactate dehydrogenase)
Bibliographic record
Abstract
We used muscle-derived lactate dehydrogenase (LDH) to measure effects of prey size and vegetation density on anaerobic foraging activity by largemouth bass (Micropterus salmoides). Largemouth bass (240–303 mm total length, TL) were fed bluegill (Lepomis macrochirus) prey (range 33–83 mm TL) in laboratory feeding trials. Prey selectivity experiments showed that small bluegills (<50 mm) were strongly preferred (>88%) over larger (>65 mm) individuals. Largemouth bass activity, as indexed by LDH, increased with increasing prey size and was 20% higher in fish feeding on large (mean size = 80 mm) versus small (mean size = 35 mm) bluegill. Bioenergetics modeling revealed that food consumption was appreciably underestimated (29%–34%) for largemouth bass foraging on large bluegills (65 and 80 mm), implying that activity costs vary with prey size, consistent with LDH measurements. In contrast to prey size, vegetation density had little effect on anaerobic energy expenditure of largemouth bass. For two size groups of largemouth bass (mean = 244 and 316 mm) foraging on 50 mm bluegill, mean LDH activity was similar across simulated vegetation densities ranging from 70 to 350 stems·m–2. These findings highlight the importance of prey size on foraging costs by piscivores and the difficulties of accounting for activity level in bioenergetics models.
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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 | 0.001 | 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".