Food selection by the white-footed mouse (<i>Peromyscus leucopus</i>) on the basis of energy and protein contents
Bibliographic record
Abstract
We studied the combined effects of energy and protein contents on food selection by the white-footed mouse, Peromyscus leucopus. Our objective was to determine how variation in levels of a food component affected the overall attractiveness of a food to the mice and whether trade-offs are made in selecting dietary protein compared with energy. We conducted food-selection tests in the laboratory and field using foods containing different combinations of energy and protein levels. In the laboratory, P. leucopus were offered a choice between a high-energy and a low-energy food with the protein level held constant and between a high-protein and a low-protein food with the energy level held constant. The field study was set up to offer an encounter with all four combinations of energy and protein simultaneously. We also tested for preferred levels of protein using pairwise food-selection tests in the laboratory. In the laboratory and field tests, P. leucopus selected high-energy foods that were low in protein, but avoided all foods with a high protein content. When given pairwise choices among foods containing 5, 15, 25, or 35% protein, P. leucopus consistently preferred the food with 15% protein. Peromyscus leucopus quickly detected differences in protein and energy levels in foods, selecting specific foods according to metabolic profitability and nutrient needs.
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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 | 0.000 |
| 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".