Environmental impacts associated with the removal of productivity-enhancing technologies from three different beef steer post-weaning management systems
Bibliographic record
Abstract
Environmental impacts of recently weaned Angus–continental steers raised without (NAT) or with (CON) productivity-enhancing technologies (PETs) in the form of hormonal implants (Ralgro®, Revalor®-G Revalor®-S, tylosin, monensin) were investigated. Heavy steers (295 ± 11 kg; SD) were direct finished, Medium steers (250 ± 11 kg) were confined backgrounded (98 ± 8 days) before finishing, and Light (205 ± 11 kg) steers were backgrounded (195.5 ± 7.5 days) and summer-grazed (67.5 ± 12.5 days) before finishing. Use of PETs resulted in six management strategies ( n = 40 hd treatment−1): Heavy conventional (HCON), Heavy natural (HNAT), Medium conventional (MCON), Medium natural (MNAT), Light conventional (LCON), and Light natural (LNAT). The NAT steers were not implanted, while the CON steers were implanted at arrival, at the start of backgrounding and finishing. Steers were finished to a target weight of 646 kg. CON steers had 10%–13% lower greenhouse gas emissions (kg CO2e), 10%–32% lower NH3 emissions, 9%–22% lower land requirements (ha), and 12%–25% lower water use (m3) per kg boneless beef compared to NAT steers. Further, HCON steers had a lower environmental impact compared to MCON and LCON. In conclusion, PETs lowered the environmental impact of all beef production systems.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".