The Importance of Policy in Emissions Inventory Accuracy— A Lesson from British Columbia, Canada
Bibliographic record
Abstract
Actual atmospheric emissions in northeast British Columbia, Canada, are much higher than reported emissions. The addition of upstream oil and gas sector sources not included in the year-2000 emissions inventory of Criteria Air Contaminants (CACs) increases annual totals of nitrogen oxides, sulfur oxides, and volatile organic compound emissions by 115.1, 89.9, and 109.5%, respectively. These emissions arise from numerous small and unregulated point sources (N = 10,129). CAC summaries are given by source type and source sector. An analysis of uncertainty and reporting policy suggests that inventory omissions are not limited to the study area and that Canadian pollutant emissions are systematically underestimated. The omissions suggest that major changes in reporting procedures are needed in Canada if true estimates of annual pollutant emissions are to be documented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".