Influences of winter supplemental feeding on the energy balance of white-tailed deer fawns in New Hampshire, U.S.A.
Bibliographic record
Abstract
The purpose of this study was to determine the influence of supplemental feeding on the energy balance of white-tailed deer (Odocoileus virginianus) in northern New Hampshire, U.S.A., during JanuaryMarch 1997. We measured the field metabolic rate (FMR) and energy balance of 10 (5 males and 5 females) supplementally fed wild fawns with doubly labeled water over 1921 days. We tested whether percent acid detergent fiber (ADF) and percent neutral detergent fiber in deer fecal samples predicted the proportion of supplemental feed (pelleted concentrate) in the diet of deer. The mean FMR of fawns was 758.4 kJ·kg0.75·d1 (range = 535.91032.8 kJ·kg0.75·d1), or 2 × their basal metabolic rate (BMR). The mean FMR of male fawns was >30% higher than that of female fawns. Percent body fat (12.1 ± 1.4% (mean ± SE)) and mass loss (3.0 ± 0.9%) varied among fawns, suggesting that an individual high FMR was not detrimental to energy balance and was related to availability of feed. We estimated that a high FMR (>2 × BMR) could be maintained only if fawns consumed about 1 kg of supplemental feed daily. Radiotelemetry data indicated that the number and juxtaposition of feeding sites in an area probably influenced home range and activity of deer. Percent ADF in feces provided the best prediction of percent grain in the diet (% grain = 0.048(% ADF)2 + 1.523(% ADF) + 96.467; r2 = 0.69) and was useful for identifying populations consuming supplemental pelleted concentrate. Biologists should expect that the influence of winter feeding on energy balance and survival will vary according to the interrelationships of deer density, food availability, and winter severity at feeding 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 | 0.000 | 0.000 |
| 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".