Modelling environmental impacts associated with the removal of productivity-enhancing technologies from Canadian feedlots: a case study
Bibliographic record
Abstract
Greenhouse gas (GHG) and ammonia (NH 3 ) emissions, land and water use associated with feedlot cattle ( n = 40 hd treatment −1 trial −1 ) treated with or without productivity-enhancing technologies were modelled for a multiyear study ( n = 4). Heifers (H) were assigned to the following treatments: (1) implanted (HTBA); (2) provided with melengestrol acetate (HMGA); (3) nonimplanted control, weight-adjusted (CON_Adj) to achieve the same final carcass weight (CW) as 1 (HCON_AdjTBA); or (4) CON_Adj to achieve the CW as 2 (HCON_AdjMGA). Steers (S) were assigned as follows: (1) implanted (STBA); (2) implanted and provided with ractopamine hydrochloride (SRAC; conducted in the last 2 years); (3) CON_Adj to achieve the same CW as 1 (SCON_AdjTBA); or (4) CON_Adj to achieve the same CW as 2 (SCON_AdjRAC). The GHG and NH 3 emissions from HTBA, HMGA, STBA, and SRAC were 3.8%, 3.0%, 10.1%, and 8.5% lower and 4.3%, 2.9%, 7.4%, and 7.6% lower, respectively, than the respective control cattle. The land required to produce feed was also reduced by 6.6%, 4.8%, 9.9%, and 10.9%, while water use was reduced by 6.4%, 4.8%, 10.1%, and 11.1% for HTBA, HMGA, STBA, and SRAC, respectively. This modelling study clearly demonstrates that conventional beef production systems have a lower environmental footprint than nonconventional systems.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".