Growth-enhancing technologies: a strategy to reduce the environmental footprint of Canadian beef production
Bibliographic record
Abstract
An examination of the relationship between growth-enhancing technologies (GET’s) and the environmental footprint of beef production systems revealed that cattle backgrounded and finished with GET’s had 3 to 7% lower GHG emissions (kg CO₂e kg boneless beef⁻¹) and 3 to 8% lower NH₃ emissions (kg NH₃ kg boneless beef⁻¹). In addition, GET-treated cattle required 5 to 11% less land (ha kg boneless beef⁻¹) and 6 to 12% less water (m³ H₂O kg boneless beef⁻¹) compared to GET-free cattle. These environmental impacts, along with economic viability and consumer preference and acceptance, must be assessed in a whole-system approach to determine the long-term sustainability of GET-free production in Canadian beef production.
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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 | 0.000 | 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 | 0.000 |
| 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".