Effects of Alcohol Ethoxylate and Pluronic Detergents on the Development of Pasture Bloat in Cattle and Sheep
Bibliographic record
Abstract
A series of studies was conducted to determine the efficacy and possible modes of action of a water-soluble mixture of alcohol ethoxylate and pluronic detergents (AEPD; Blocare 4511, ANCARE, Auckland, NZ) in preventing pasture bloat in ruminants grazing or fed freshly harvested alfalfa. Ten cannulated Suffolk wethers were offered freshly harvested alfalfa; five were given a daily intraruminal dose of 40 ml of 50% AEPD (vol/vol) 1 h before feeding, and five (controls) were dosed with water. Viscosity of ruminal fluid was reduced (P < 0.001) in AEPD-treated wethers, relative to the controls, for the first 2 h after feeding but not at 4 h after feeding and beyond. Treatment with AEPD did not affect dry matter (DM) intake, digestibility of DM, acid detergent fiber, or neutral detergent fiber, or N digestion and retention, implying that AEPD likely would not affect milk production. In a crossover grazing study, five of the wethers were given AEPD in drinking water (0.1%, vol/vol); treatment with AEPD was 100% effective for preventing bloat in sheep grazing early-bloom alfalfa for 4 h daily. Replicate grazing studies were conducted with cattle in Lethbridge, AB; Lacombe, AB; and Kamloops, BC. Treated animals received AEPD in the water (0.06%, vol/vol) and grazed vegetative alfalfa for 6 h daily. As it did with sheep, AEPD treatment effectively precluded the bloat observed in control animals. Consequently, AEPD may be a valuable tool for alfalfa pasture-based dairy production although further study is required to develop an integrated model for optimal administration under a variety of climatic conditions.
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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".