THE ALLOMETRY OF PLANT SPACING THAT REGULATES FOOD INTAKE RATE IN MAMMALIAN HERBIVORES
Bibliographic record
Abstract
The distance that mammalian herbivores can travel without interrupting food processing corresponds to a distance threshold (d*) in plant spacing where change occurs in the mechanisms regulating the functional response. The instantaneous rate of food consumption is controlled by food encounter rate when plant spacing exceeds d*. Below this threshold, food processing in the mouth controls instantaneous intake rate. The distance threshold provides a mechanistic definition of the scale of heterogeneity of the food resource. Recent work indicates that d* should scale positively with the body mass of mammalian herbivores. Here I evaluated the empirical evidence for this positive scaling by investigating (1) herbivores consuming only alfalfa (Medicago sativa), (2) grazers, and (3) herbivores consuming any kind of vegetation. Overall, I found greater evidence for a negative than for a positive scaling of d*. Out of the three groups, only herbivores consuming alfalfa could yield a positive covariation between d* and body mass. However, even this positive scaling became negative when herbivores consumed bites of alfalfa that were only a fraction of their maximum size. Finally, d* also scaled negatively among herbivores foraging in similar food patches. Overall, differences in foraging decisions among mammalian herbivores seem more likely to have been shaped by a negative than a positive scaling of d*.
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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 | 0.001 | 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".