Areas and Greenhouse Gas Emissions from Feed Crops Not Used in Canadian Livestock Production in 2001
Bibliographic record
Abstract
Estimates of greenhouse gas (GHG) emissions from Canada's four main livestock industries were integrated with the Canadian Economic and Emissions Model for Agriculture (CEEMA) which operates at the census district level. The livestock crop complex (LCC), which defines the crop area required to feed Canada's livestock, was disaggregated from provincial to district level. The LCC areas were subtracted from the crop areas stored in the CEEMA database to define the maximum area available for non-meat food, fiber, and biofuel feedstock production. The resulting non-livestock residual (NLR) area estimates were 18.7 Mha in the west (excluding rangeland, summerfallow, irrigated cropland and any crops not associated with livestock diets) and 1.0 Mha in the east. The GHG emissions from the NLR in the west were 13.7 Tg CO2e, or 30% of the total GHG emissions from those crops associated with livestock diets. The 1.6 Tg CO2e of GHG from the NLR in Eastern Canada represented 8% of the total GHG emissions from those livestock-related crops. The eastern NLR crop areas were more sensitive to changes in livestock populations than the Western Canada NLR areas because of the more dominant role of livestock production in eastern Canadian agriculture than in the west. The total agricultural GHG emissions budget showed direct but muted sensitivity to changes in Canadian livestock populations in both eastern and Western Canada. The methodology will link agricultural GHG emissions with district level land use decisions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".