Page 1of 8 The Evolution of Air Emissions Inventories in
Bibliographic record
Abstract
The monitoring of air quality to develop meaningful air programs and policies has been an on-going initiative in the province of Ontario, Canada. Ontario has been monitoring and reporting on air quality throughout the province since the 1970s. In 1993 the Ontario Ministry of the Environment (MOE) embarked on its first effort to inventory Ontario’s point source emissions through the use of a voluntary survey. This survey was distributed annually to industrial facilities throughout the province, requesting annual activity data for the estimation of Ontario facility air emissions. This exercise consistently yielded a low response from Ontario point sources. In January of 2000, the MOE proposed an air quality initiative that would consist of a regulation requiring the mandatory monitoring and reporting of air emissions from point sources. This proposal became reality when Ontario Regulation 227 (O. Reg. 227/00- "Electricity Generation- Monitoring and Reporting") came into effect in May of 2000. This regulation applied to certain Ontario electricity generation facilities and regulated 28 airborne contaminants. The goal of a regulated air emissions inventory program applicable to a wide spectrum facilities was realized when the MOE promulgated Ontario Regulation 127 (O.Reg.127/01) regulating air emissions from industrial, commercial, institutional, and municipal point sources in the province. Facilities are able to report their air emissions under O.Reg.127/01 via the MOE’s web-based reporting and registration site titled “OnAIR”. This study will chronicle the evolution of air emissions inventories in the province of Ontario, describing the progression from voluntary to mandatory point source air emissions reporting, provide observations of the air emissions reports submitted by Ontario point sources, and discuss the future direction of air emissions reporting in the province.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".