Effects of conventional and natural management systems on backgrounding and finishing performance of beef steers
Bibliographic record
Abstract
This study evaluated 2 beef cattle manage- ment strategies, conventional (CONV) or natural (NAT), for 3 BW groups at weaning to evaluate growth perfor- mance during backgrounding and finishing and carcass characteristics. A total of 240 weaned steers, free of preweaning implants, were allocated into heavy (HV) (290 ± 21 kg; mean ± SD), medium (MD) (247 ± 8 kg), or light (LT) (214 ± 15.6 kg) weight groups over 2 yr. Each weight group (n = 80) was randomly allo- cated to 1 of 2 treatments (n = 40): either conventional or natural (n = 4). Conventional treatments used hormonal implants and feed additives, but the natural treatment did not. Following a 41-d receiving phase, HV steers entered direct finishing, MD steers entered a short backgrounding and finishing, and LT steers entered a long backgrounding, grazing, and finishing, all fed to a shrink weight of 620 kg. Steer ADG was 19% and 22% greater for MD- and LT-CONV, respectively, at backgrounding, compared with NAT. The G:F was 20% greater for HV- and MD-CONV at finishing and 25% greater for LT-CONV at backgrounding. The HV-, MD-, and LT-CONV took 50, 71, and 59 fewer days on feed, respectively, to finish, relative to NAT. The rib-eye area were greatest in HV-CONV, and NAT produced greater marbling, QG (AAA), and backfat thickness and had a greater proportion of liver abscesses. Steers managed without performance-enhancing technologies under west- ern Canadian conditions will have lower ADG, G:F, and YG1, but greater days on feed to a target weight, AAA grade, marbling, and backfat thickness, than convention- ally-managed steers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 teacher head, 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".