Effects of Feeding a Mycotoxin Binder on Nutrient Digestibility, Alkaloid Recovery in Feces, and Performance of Lambs Fed Diets Contaminated with Cereal Ergot
Bibliographic record
Abstract
As contamination with cereal ergot has been increasing in western Canada, this study evaluated impacts of feeding a mycotoxin binder (Biomin® II; BB) on nutrient digestibility, alkaloid recovery in feces, and lamb growth performance. Forty-eight ram lambs (25.9 ± 1.4 kg) were randomly assigned to one of four barley-based diets: Control (C), no added alkaloids, Control + BB fed at 30 g/head per day (CBB); Ergot, 2564 ppb total R + S epimers (E); Ergot + BB, 2534 ppb R + S epimers (EBB). Lambs were fed ab libitum for up to 11 weeks until slaughter at >46 kg live weight. Both average daily gain (ADG) and gain/feed ratio were greater (p < 0.01) for lambs fed C and CBB diets as compared with those containing added ergot, although dry matter intake was not affected by dietary ergot or BB. Serum prolactin concentrations were two times higher in EBB- compared with E-fed lambs (p < 0.05), although both were lower than in C or CBB (p < 0.001) lambs. Rectal temperatures were greater in lambs receiving dietary ergot (p ≤ 0.001) than in C- and CBB-fed lambs. In a digestibility study using eight ram lambs, treatment with BB increased neutral detergent fiber (NDF) digestibility (p = 0.01). Nitrogen retention (g) was greater (p < 0.05) for lambs receiving C or CBB compared with ergot-contaminated diets. Feces of EBB lambs had 38.5% greater (p < 0.001) recovery of alkaloids compared with those fed E. Based on sparing of prolactin, BB may reduce impacts of ergot alkaloids by increasing their excretion in feces. Accordingly, concentrations of dietary alkaloids, which would not harm sheep, would be increased by feeding BB.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".