Quantifying Methane Emissions from the Greater Toronto Area
Bibliographic record
Abstract
The government of Ontario has committed to reduce its greenhouse gas (GHG) emissions by 30% of2005 levels by 2030. The Greater Toronto Area (GTA, pop. 6.4 million) is the most populous city in Canada, thus accurately quantifying GHG emissions from the GTA is an important step towards meeting Ontario’s commitments. In order to quantify GHG emissions and emission trends in an urban area, it is important to monitor GHG concentration levels in the atmosphere regularly to verify the accuracy of the reported emissions. In this study, I develop a new methane (CH4) emission inventory, using individual facility reports and emission estimates from area sources gathered for each municipality in the GTA. This allows us to have an emission inventory with a high spatial resolution that can be evaluated by atmospheric measurements. I describe the development of a network of portable Fourier Transform Spectrometers (FTS) in the GTA that measure total columns of CO2, CH4 and CO in the atmosphere and use the tracer-tracer ratio method to estimate CH4 emissions in the GTA. In addition, I preform a detailed assessment of the FTS instrument accuracy, precision and their optical stability using data collected during a field campaign at 6 TCCON stations in North America. I also demonstrate that the low-resolution FTSs could be useful as a “travelling standard” to indirectly compare TCCON instruments to each other. Coincident measurements of the vertical profile of CO2, CH4, and CO from the AirCore balloon platform provided the data required to tie the FTS retrievals to the WMO trace gas standard scale so that they can be used as a satellite validation tool.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".