Combination of long-chain alcohols and fatty acids with alkanes as faecal markers to estimate feed intake and digestibility in horses and cattle fed on grass-heathland vegetation communities
Bibliographic record
Abstract
Application of long-chain alcohols (LCOH) and long-chain fatty acids (LCFA) combined with alkanes as faecal markers to estimate dry matter intake (DMI) and dry matter digestibility (DMD) of equines and cattle was assessed. Six crossbred mares, randomly divided in two groups (H1 and H2), and three nonlactating cows (C) were housed in individual stalls. Groups H1 and C were fed on a diet of ryegrass (Lolium perenne, 0.7) and heather (Erica spp., Calluna vulgaris, 0.3), and H2 received ryegrass (0.4), heather (0.3) and gorse (Ulex gallii, 0.3). Digestibility was estimated using LCOH (C28-OH and C30-OH) and LCFA (C28-FA, C30-FA, and C32-FA) as internal markers. For DMI estimation, animals received daily a paper pellet containing C24, C32, and C36n-alkanes. Intake was estimated from the faecal ratio of naturally occurring LCOH, LCFA, and dosed n-alkanes, and was compared with the known DMI values. In horses, all markers provided accurate estimates of DMD. Similarly, LCOH provided accurate estimates of DMD in cattle, whereas LCFA underestimated it (P < 0.05). Intake estimates were affected (P < 0.05) by the marker pair used in calculations. In general, the C24:C24-FA pair provided the most accurate DMI estimates. Results suggest the usefulness of combining epicuticular compounds as faecal markers to estimate DMI, DMD and diet composition of horses and cattle grazing grass-heathland communities, simultaneously.
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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".