Greenhouse Gas Emissions from Canadian Agriculture: Estimates and Measurements
Bibliographic record
Abstract
Greenhouse gases (GHGs) cause the warming of the planet’s surface. Although this warming is vital for life on Earth, accelerated surface temperature rises due to increased GHGs in the atmosphere result in increasing atmospheric energy and rates of evaporation, causing unpredictable weather patterns and more intense weather events. The main GHGs are carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Of the 729 Megatonnes (Mt) of CO2 equivalent (CO2 eq) emitted by GHGs in Canada in 2018, 59 Mt were emitted by the agricultural sector in the form of CO2, N2O and CH4. The largest GHG emissions come from CH4 through enteric fermentation of beef and dairy cattle. Most N2O emissions come from agricultural soils through direct and indirect releases into the atmosphere. Carbon dioxide was also emitted after lime and urea applications as well as with the use of fossil fuel combustion machinery. Field techniques and empirical and process models have been developed to estimate and validate GHG emissions for different farm scenarios. These models aim to simulate every component of a farming system, whether a large beef cattle operation or a small animal and crop farm. Consequently, the models are constantly being assessed and revised as more data are available and methodologies are improved. As we gain better understanding of agricultural GHG emission estimates for different farm scenarios, the next step is to target emission sources and find ways to decrease emissions while maintaining or improving the financial sustainability of the farm and production system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".