<i>n</i>-Alkane and long-chain alcohol recovery in moose (<i>Alces</i> <i>alces</i>), a browsing herbivore
Bibliographic record
Abstract
Habitat management for herbivores often depends on an understanding of the food habits of animals. Plant cuticular waxes containing nearly indigestible complex mixture of n-alkanes and long-chain alcohols (LCOHs) have recently shown promise for diet analyses, but the accuracy of the technique depends strongly on the efficiency of recovery of the markers in feces. Fecal recovery of n-alkanes and LCOHs from 10 browse stems or leaves and two ensiled grass hays fed to moose (Alces alces (Linnaeus, 1758)) during in vivo digestion trials was investigated. n-Alkanes and LCOHs were extracted using a single-step accelerated solvent extraction technique and the recovery of these cuticular components was calculated from the feces of the animals. n-Alkane recoveries from feces averaged 0.82, ranging from a low of 0.58 (haylage) to a high of 0.95 (browse stems). LCOH recoveries averaged 0.92 across all forages, ranging from 0.80 (haylage) to a high of 1.13 (browse stems). n-Alkane and LCOH fecal recovery increased with increasing chain length, similar to findings in other studies. Although fecal recovery of n-alkanes and LCOHs were variable, we conclude that they are inversely related to forage digestibility, are consistent within forage classes, and are therefore predictable markers for use in assessing herbivore diets.
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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) | 0.000 | 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".