Impact of calving seasons and feeding systems in western Canada. I. Postweaning growth performance and carcass characteristics of crossbred steers
Bibliographic record
Abstract
Durunna, O. N., Block, H. C., Iwaasa, A. D., Thompson, L. C., Scott, S. L., Robins, C., Khakbazan, M. and Lardner, H. A. 2014. Impact of calving seasons and feeding systems in western Canada. I. Postweaning growth performance and carcass characteristics of crossbred steers. Can. J. Anim. Sci. 94: 571-582. Crossbred steers (n=272) weaned from early (EC) and late (LC) calving systems (CS) were used to evaluate the impact of two feeding systems (FS) on postweaning performance and carcass characteristics. The steers were randomly assigned to either a rapid-gain feeding (RF) or a slow-gain feeding (SF) system. The RF steers were managed to have body weight (BW) gain of 1 kg d-1 on a silage-hay diet during the backgrounding period prior to finishing, while the SF steers were first backgrounded on a hay diet (gain of 0.7 kg d-1), then grazed alfalfa-meadow bromegrass pasture and annual cereal swaths prior to finishing. All treatment groups received a conventional diet during finishing until the steers attained a target backfat thickness or BW or both. There was no difference (P=0.48) between the two FS for the average age of the steers at the beginning of the experiment. There was a CS×FS effect (P<0.01) on the age at slaughter, where the steers in the EC-RF, EC-SF, LC-RF and LC-SF were 426, 659, 504 and 606 d, respectively. The longer time on feed for LC-RF steers compared with EC-RF suggests the potential effect of summer ambient temperatures at finishing. The EC-RF group had the least carcass fat thickness (P<0.01), but there was no main or interaction effect (P>0.08) on dressing percentage or lean meat yield. There was a FS effect (P<0.05) on meat colour and marbling texture where SF steers had more desirable meat colour and marbling texture. Beef producers adopting EC-RF would finish their calves earlier but at a lighter weight.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".