Retention time of macerated alfalfa hay and silage in sheep.
Bibliographic record
Abstract
Fresh alfalfa was mowed and conditioned mechanically at four levels: a control (rubber rolls), macerated once (a single passage through three finely corrugated rolls set at 1-mm clearance), macerated twice (two passages), and macerated thrice (three passages). Alfalfa was then field-wilted either for 45 h and conserved as chopped silage at 30% dry matter (DM) or for 94 h and stored as baled hay at 85% DM. The eight forage treatments (four mechanical conditioning levels x two conservation systems) were fed to 24 sheep (three replications per treatment) during 5 wk. At the beginning of wk 5, a 15-g sample of chromium-mordanted forage (3.5% Cr) was fed to each sheep, and feces samples were collected at 30 different times over 7 d, between 10 h and 168 h after Cr ingestion. Four models were used to estimate the passage rates, the time delay, and the mean retention time (MRT). A two-compartment time-dependent model and a multicompartment model produced the best fit (average r2 of 0.96) to represent the Cr concentration in the feces over time. When compared with alfalfa hay, alfalfa silage had a higher (P < 0.01) time-dependent turnover rate (0.0949 vs 0.0733/h), a lower (P = 0.03) time delay (9.1 vs 11 h), and a lower (P = 0.04) MRT (57.8 vs 64.4 h). Maceration did not affect significantly (P > 0.10) the time delay or the MRT. However, the MRT of macerated alfalfa hay tended to be higher than the MRT of control hay. Experimental data based on marker concentration in the feces can be used satisfactorily to assess differences in MRT between treatments, but they should be used with caution to estimate the partition of retention time within the gastrointestinal tract.
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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 | 0.001 | 0.001 |
| 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".