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 (NH3) 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 NH3 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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".