Using <i>n</i>-alkane markers to estimate forage intake of mule deer
Bibliographic record
Abstract
Dry matter intake (DMI) of free-ranging mule deer ( Odocoileus hemionus (Rafinesque, 1817)) in the aspen parkland of east-central Alberta was estimated using the double n-alkane ratio and bite count methods. Eleven female mule deer were given an intraruminal capsule containing synthetic n-alkanes and released into 4–8 ha paddocks. Known concentration of even-chained carbon (C32) was released from the capsules and recovered 7–10 days later from freshly voided faeces. Odd carbon chains of adjacent length (C31:C33) were extracted from vegetation samples gathered during behavioural observations of four tame deer. Calculations from the pairings (C31:C32; C33:C32) provided estimates of DMI that ranged from 1.29 to 2.73 kg/day. DMI was highest during autumn, possibly to prepare for increased energy demands for the upcoming winter. No differences were found in seasonal or annual comparisons of bite rates, but bite sizes differed in all comparisons, suggesting bite size was a critical factor affecting consumption rates for mule deer on northern ranges. Maximum bite sizes rose from 49 ± 5 mg/bite in July to 213 ± 58 mg/bite in October. Maximum consumption rates were in October of both years (3.6–4.4 g/min) and occasionally approached theoretical maxima (14.3–15.6 g/min).
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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) | 0.000 | 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".