PSXII-31 Severity of hay processing on dry matter intake, sorting behaviour, and apparent total tract digestibility in lambs
Bibliographic record
Abstract
Abstract The objective was to evaluate digestibility and sorting behaviour of grass hay processed to differing severities. Thirty-six wether lambs were used in a completely randomized design and fed diets consisting of grass hay (82.2 %), whole barley (15.7 %), and a mineral-vitamin supplement (2.1 %). Treatments included: unprocessed hay (CON); shredded hay (SHRED); chopped hay (CHOP); and ground hay (GRIND). Lambs were fed their respective diet for 20 d followed by 4 d for measurement of feed intake and fecal output. Lambs fed CON (1.23 kg and 3.24 %) had greater DMI (P = 0.04 and 0.05) compared to CHOP (1.04 kg and 2.71 % BW), with SHRED (1.17 kg and 3.11 % BW) and GRIND (1.13 kg and 2.97 % BW) being intermediate. Undigestible NDF intake tended to decrease as processing severity increased (P = 0.05). Dry matter digestibility (67.6, 66.2, 59.6, and 60.8 % for CON, SHRED, CHOP, and GRIND, respectively; P < 0.01) generally decreased as the severity of forage processing increased. Crude protein digestibility was greatest in CON (68.6 %) compared to SHRED (60.83 %), CHOP (58.7 %), and GRIND (58.5 %; P < 0.01). ADF and aNDFom digestibilities were greater for CON (57.4 and 67.2 %) and SHRED (60.0 and 67.9 %) compared to CHOP (44.8 and 54.6 %) and GRIND (48.3 and 58.5 %; P < 0.001). CON and SHRED lambs sorted for larger particle sizes (particles > 19 mm and 8 to 19 mm; P < 0.001 and 0.025, respectively) while CHOP and GRIND lambs sorted for smaller particles (particles 4 to 8 mm and particles < 4 mm; P < 0.001 and 0.003, respectively). These results indicate that processing of grass hay does not increase nutrient digestibility partly because of the sorting behaviour of lambs.
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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".