173 Greenhouse gas emissions and land use associated with the removal of growth-enhancing technologies from backgrounding and finishing cattle in Canada: A case study
Bibliographic record
Abstract
Abstract Greenhouse gas emissions from backgrounding and finishing cattle with and without the use of growth-enhancing technologies (GET) were estimated using a whole-farm model, Holos (www.agr.gc.ca/holos-ghg). Model inputs were obtained from a four-year performance study using heifers (H) and steers (S) with six treatments (n = 40 hd treatment-1 yr-1): 1) H control (HCon); 2) H implanted (HTBA); 3) H melengestrol acetate (HMGA); 4) S control (SCon); 5) S implanted (STBA); and 6) S implanted + ractopamine hydrochloride (SRAC; conducted in the last two years). All cattle were finished to achieve a consistent number of days on feed (DOF; n = 233 ± 8). Lighter finish weights were observed for HCon and SCon. As a result, DOF were adjusted (-1 to 65 d) to achieve the same final weight as GET-treated cattle. Total emissions (kg CO2e head-1) were greater for Hcon_AdjHTBA (2967 ± 183) and HCon_AdjHMGA (2766 ± 84), than HTBA (2897 ± 184) and HMGA (2730 ± 81). Similarly, total emissions (kg CO2e head-1) for SCon_AdjSTBA (3169 ± 192) and SCon_AdjSRAC (3252 ± 202) were greater than STBA (2998 ± 153) and SRAC (3097 ± 185), respectively. On an intensity basis, (kg CO2e kg slaughter weight-1), emissions from GET-treated cattle [HTBA (4.49 ± 0.19), HMGA (4.39 ± 0.13), STBA (4.28 ± 0.17), and SRAC (4.31 ± 0.25)] were lower than HCon_AdjHTBA (4.60 ± 0.21), HCon_AdjHMGA (4.45 ± 0.18), SCon_AdjSTBA (4.52 ± 0.23), and SCon_AdjSRAC (4.52 ± 0.28), respectively. Furthermore, land-use (ha 100 kg slaughter weight-1) was reduced for GET-treated cattle (HTBA (0.068 ± 0.001), HMGA (0.067 ± 0.001), STBA (0.066 ± 0.001), and SRAC (0.067 ± 0.000)) compared to HCon_AdjTBA (0.071 ± 0.001), HCon_AdjMGA (0.069 ± 0.002), SCon_AdjTBA (0.072 ± 0.002), and SCon_AdjRAC (0.0073 ± 0.001), respectively. This study demonstrates that GETs can reduce the environmental footprint of beef cattle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".