Photosynthetic pigments estimate diet quality in forage and feces of elk (<i>Cervus</i> <i>elaphus</i>)
Bibliographic record
Abstract
Understanding the nutritional dynamics of herbivores living in highly seasonal landscapes remains a central challenge in foraging ecology with few tools available for describing variation in selection for dormant versus growing vegetation. Here, we tested whether the concentrations of photosynthetic pigments (chlorophylls and carotenoids) in forage and feces of elk (Cervus elaphus L., 1785) were correlated with other commonly used indices of forage quality (digestibility, energy content, neutral detergent fiber (NDF), and nitrogen content) and diet quality (fecal nitrogen, fecal NDF, and botanical composition of the diet). Photosynthetic pigment concentrations were strongly correlated with nitrogen content, gross energy, digestibility, and NDF of elk forages, particularly in spring. Winter and spring variation in fecal pigments and fecal nitrogen was explained with nearly identical linear models estimating the effects of season, sex, and day-of-spring, although models of fecal pigments were consistently a better fit (r2adjusted = 0.379–0.904) and estimated effect sizes more precisely than models of fecal nitrogen (r2adjusted = 0.247–0.773). A positive correlation with forage digestibility, nutrient concentration, and (or) botanical composition of the diet implies fecal photosynthetic pigments may be a sensitive and informative descriptor of diet selection in free-ranging herbivores.
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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".