An assessment of the environmental sustainability of beef production in Canada
Bibliographic record
Abstract
This study assessed the environmental impacts of beef cattle production and their effects on the overall sustainability of Canadian beef production. Cradle to farm gate, cradle to processor’s gate, and cradle to consumer plate life cycle assessments were carried out to quantify greenhouse gases (GHG), resource use (i.e., water, land, and fuel), and potential water and air pollution (i.e., freshwater eutrophication, terrestrial acidification, and photochemical oxidants formation). Across the production chain, feed production had the greatest impact on most environmental indicators. The GHG intensity without dairy meat was estimated as 10.4 kg CO2-eq per kg of live weight (LW), corresponding to 32.8 kg CO2-eq per kg of consumed boneless beef. Including dairy meat reduced GHG intensity by 5.8% (0.6 kg CO2-eq kg LW–1) compared to when it was excluded. Other environmental metrics per kg of LW were 657 L, 38.7 m2 annual crop-eq, 0.4 kg oil-eq, 2.6 kg P-eq, 115.9 kg SO2-eq, and 8.7 kg NOx-eq for water use, land use, fossil fuel use, freshwater eutrophication, terrestrial acidification, and photochemical oxidants, respectively. Data provide benchmarks for use in future regional and national assessments that are designed to encourage the adoption of sustainable management practices that can lower the environmental footprint of Canadian beef production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".