Long-term trends in the greenhouse gas emissions from the Canadian dairy industry
Bibliographic record
Abstract
Estimates of the efficiency of mitigation measures on reducing greenhouse gas (GHG) emissions from the agricultural sector are required. In this paper, recently calculated dairy GHG emissions for 2001 were extrapolated back to 1981 for census years using an index. The index was verified by comparing it with estimates based on the Intergovernmental Panel on Climate Change (IPCC) methodology for 1991. The index agreed with the IPCC estimates within 1% for methane and 4% for nitrous oxide on a national scale with no region having a difference of more than 5% for methane. For nitrous oxide, all regions were within 10%, except British Columbia, where the index was 19% too high. The index indicates that GHG emissions from primary milk production within the Canadian dairy industry have decreased by about 49% since 1981, mainly due to a 57% reduction in the dairy cow population during that period. The GHG emissions per kilogram of milk decreased by 35%, that is from 1.22 kg CO2eq kg-1 milk to 0.91 kg CO2eq kg-1 milk. Because this study took into account the energy-related CO2 emissions from all the major farm inputs (fertilizer and fossil fuel), there was little risk of hidden GHG emissions in the emission intensity calculation. This study demonstrates that where lack of input data restricts historical application of simulation models, a semi-empirical index approach can yield valuable results. Key words: Greenhouse gas, dairy industry, index, intensity indicator
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".