Influence of dietary methionine concentration on growth and nitrogen balance in weanling Quarter Horses
Bibliographic record
Abstract
Twenty-four Quarter Horse weanlings (120 ± 10 d) were blocked by age into 4 groups (n=6) for a 56-d trial to evaluate the influence of dietary Met concentration on growth and N balance. Weanling horses were housed by block and individually fed concentrates twice daily at 1.75% BW (as-fed basis). Weanling horses were randomly assigned to 1 of 4 concentrate treatments: basal (0.20 Met), basal + 0.03% Met (0.23 Met), basal + 0.07% Met (0.27 Met), and basal + 0.11% Met (0.31 Met). Diets were formulated to be isonitrogenous and isocaloric and contained equal amounts of Lys and Thr. Coastal bermudagrass hay (Cynodon dactylon) was individually fed at 0.75% BW (as-fed basis). Growth measurements, BW, rump fat, and plasma were obtained every 7 d. The final 4 d consisted of total collection of urine and feces. Feed, fecal, and urine samples were analyzed for N content, and N balance was calculated. Urine was analyzed for urea and ammonia concentrations. Plasma was analyzed for urea concentration. Grain, hay, and fecal samples were analyzed for nutrient composition. Data were analyzed using the PROC MIXED procedure of SAS. Linear, quadratic, and cubic effects were tested in the form of contrasts. There was no influence (P>0.25) of treatment on growth measurements, N balance, or urinary urea or ammonia. Intake of Lys and Thr did not differ (P>0.08) among treatments. Methionine intake increased as expected with increasing Met inclusion in the diet (P<0.01). Plasma urea N responded quadratically (P=0.04) to treatment. An unexpected peak in PUN was observed with 0.27 Met. The results indicate future studies that can ensure Met as the only dietary variable are necessary to better explain the Met requirements of weanling horses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".