Food supplementation experiments revisited: verifying that supplemental food is used by its intended recipients
Bibliographic record
Abstract
Field-based food supplementation experiments can determine whether populations are limited by natural food availability. However, they may yield spurious results if added food is hoarded by dominants, exploited by immigrants, or delivered ineffectively. Surprisingly, population-level approaches accounting for these potential sources of bias have not been established. We explored responses of free-ranging snowshoe hares ( Lepus americanus Erxleben, 1777) to food supplementation by contrasting per capita browsing rates on three study sites with added food with those on three control sites during two winters. Food augmentation reduced foraging pressure by hares; this reduction was significant when browse species of high dietary importance were considered. By implication, hares on manipulated sites switched from favored natural foods to supplemental food, meaning that the extra food found its mark and was not heavily exploited by immigrants or hoarded by dominants. Demographic responses to food addition were not detected on the manipulated sites, indicating that the study population was not food limited. Given the success with which we detected the signal of supplementation and eliminated potential confounding factors, we suggest that future studies using food addition in the context of herbivore population dynamics would benefit from a more mechanistic approach; this may involve measurement of browsing rates on manipulated and control sites.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".