The State of Carbon Neutrality in the Greater Toronto Hamilton Area for 2016
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A methodology was developed for creating municipality-scale emission inventories using publicly available online data. A bottom-up emissions inventory of prominent greenhouse gases (carbon dioxide, methane, and nitrous oxide) and criteria air contaminants (carbon monoxide and oxides of nitrogen) in the Greater Toronto Hamilton Area (GTHA) was developed for the year 2016. Emissions from agriculture, buildings, ecosystems, industries, and transportation were estimated in layers and then summed in each individual grid square. The inventory used non-proprietary data and was distributed on a fine grid (four square kilometre grid cells). It was estimated that the GTHA produced 58.6 megatonnes of carbon dioxide equivalent, 194,460 tonnes of carbon monoxide, and 106,140 tonnes of nitrogen oxides for the study year. Traffic produced the most of all pollutants except methane, which was dominated by waste (landfills). The inventory was validated against published inventories from Environment and Climate Change Canada (ECCC) and The Atmospheric Fund. The developed methodology can be used by other municipalities to assess their state of carbon neutrality and air pollution. This dataset includes the emissions found for each grid cell, located by latitude and longitude. The exact area of each grid cell is included along with the values of carbon monoxide, nitrogen oxides, carbon dioxide, methane, and nitrous oxide. The values are normalized by population and area, respectively. Greenhouse gases are combined into megatonnes of carbon dioxide equivalent per square kilometre. A shapefile of the data distributed in grid cells over the Greater Toronto Hamilton Area in Ontario, Canada is included to aid visualization.
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.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.001 |
| 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 it