The road to net-zero in Canada through regionalization and segmentation of GHG emissions and the role of water resources management
Bibliographic record
Abstract
ABSTRACT The primary goal of this study was to assess the temporal variations, trends, and aberrations in the greenhouse gas (GHG) series for Canada from a regional and economic sector perspective. A Bayesian Change Point method based on the Markov Chain Monte Carlo method and Binary Segmentation were used to detect aberrations, the number of changes, and locations. Trend analysis and change point results show that all the Prairie provinces have uptrends and frequent change points. On the other hand, Central, Atlantic, and the Territories show evidence of a downtrend. There is a consistent uptrend and frequent change points for the economic sectors in the oil and gas, agriculture, transportation, and building economic sectors. This upward trend could be due to a consistent increase in petroleum extraction, increased population, and increased number of on-road cars. The increase in the agriculture sector could be due to an increase in livestock products and the application of fertilizer and manure for farming purposes, especially in the Prairie jurisdictions. From the foregoing, Canada's abundant water resources potential will be crucial in mitigating GHG emission in the heat and electricity sector across various jurisdictions.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".