The effects of a low lignin hull, high oil groat oat on the performance and carcass characteristics of feedlot cattle
Bibliographic record
Abstract
Arya, S. and McKinnon, J. J. 2011. The effects of a low lignin hull, high oil groat oat on the performance and carcass characteristics of feedlot cattle. Can. J. Anim. Sci. 91: 685–693. A study was conducted to evaluate inclusion levels of a low lignin hull, high oil groat oat (CDC SO-I) on the performance and carcass characteristics of feedlot cattle. Two hundred crossbred steers (average weight of 427.3 kg±22.4) fed in 20 pens (10 head per pen) were used. Five treatments, formulated by replacing barley grain with increasing levels of CDC SO-I oat (Barley grain:CDC SO-I oat ratios of 100:0; 75:25; 50:50; 25:75 and 0:100; DM basis) were used. Over the entire study, there was a linear decrease (P<0.01) in DMI and ADG with increasing inclusion level of CDC SO-I oat. There was a quadratic effect (P=0.03) on gain to feed with similar values for steers fed 100:0, 75:25, 50:50 and 75:25 and then decreasing for the 0:100 treatment. Days on feed increased (P=0.03) quadratically with steers fed the 75:25 and 0:100 treatments spending the longest time on feed. Increasing the inclusion level of CDC SO-I oat in the diet also linearly decreased (P<0.01) carcass weight, dressing percentage and carcass grade fat. However, there was no effect of treatment on l. dorsi area and lean meat yield. There was no effect (P>0.05) of treatment on marbling score. Overall, the results of this study indicate that replacement of barley grain by CDC SO-I oat in finishing diets decreases DMI and as a result leads to reduced ADG, increased days on feed and lower slaughter and carcass weights.
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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) | 0.000 | 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".