The Protein-based GHG Emission Intensity for Livestock Products in Canada
Bibliographic record
Abstract
Assessments of the total greenhouse gas (GHG) emissions and emission intensities had been carried out prior to this analysis for dairy, beef, pork, and poultry in Canada. The GHG emission intensities of these industries were based on different units of food produced. In this paper, the GHG emission intensities of the four livestock industries were compared on the basis of the weight of protein produced. The protein-based emission intensity for beef was almost four times as high as the GHG emission intensity for milk production. The emission intensities of pork production were lower than the emissions from milk production because of lower CH4 emissions. Broilers had the lowest GHG emission intensity of all five livestock commodities. The next lowest GHG intensity was for egg production. The differences between the egg and broiler intensities cannot be attributed to any one GHG. The number of breeding animals that must be maintained in order to produce one animal for slaughter is much higher for cattle than...
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".