508 Effects of starch content of the concentrate on growth performance, temperament and carcass traits of finishing bison
Bibliographic record
Abstract
Abstract Yearling bison bulls [n = 48; commercial Wood × Plains, 435 ± 13.4 kg body weight (BW)] were used in a 156-d feeding trial to evaluate the effects of the starch content in the concentrate on growth performance, gain-to-feed ratio (GF), temperament, and carcass traits. Bison were homogenously distributed into 12 pens (4 bulls/pen) based on BW, and each pen randomly assigned (n = 6) to receive either a high-starch (HS; 51.4% dry matter basis, DM), or moderate-starch (MS; 25.8% DM) concentrate, alongside free access to water and grass hay bales. The amount of feed delivered to each pen and orts were recorded to estimate dry matter intake (DMI) for each ingredient. Bison were weighed on d 1, 28, 60, 116, and 156 to monitor average daily gain (ADG) and GF. When handled for weighing, exit speed from the chute was determined as a measure of their temperament. On d 156, bison were transported to a slaughterhouse where back fat thickness, carcass grade, and ribeye area were determined. The HS bison bulls had reduced DMI for both grass hay (P = 0.04) and concentrate (P < 0.01) compared with MS. The HS bulls had a greater (P < 0.01) chute exit speed than MS. Both MS and HS had similar (P > 0.10) final BW, ADG, GF, and proportion of hay and concentrate inclusion in the diet. Back fat thickness, ribeye area, and proportion of carcass grades were not different (P > 0.10) between MS and HS. These findings suggest that finishing bison bulls reduced their feed intake in response to a high percentage of starch in the concentrate, without affecting growth performance, GF, or carcass traits indicative of fat and muscle deposition, but with HS causing a more excitable temperament profile, typically associated to chronic stress.
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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 | 0.001 |
| 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".