Bibliographic record
Abstract
Systematic air quality measurements were made in the study region of Toronto during the 2015 Pan-Am games (July 14th – July 29th). These measurements were done using the mobile lab CRUISER (Canadian Regional and Urban Investigation System for Environmental Research) to identify emission sources for characterization and emission inventory development. The measurements included several trace gases (NOx, SO2, CO, VOCs, CO2, CH4, and black carbon), air toxics (H2S, aromatics), atmospheric particles, particle composition and meteorological parameters. Additionally, the measurements were systematically designed to cover 18 sub-regions in and around the Greater Toronto Area multiple times so as to cover the different exposure settings such as traffic, industry, residential, commercial, etc. These data aim to provide a good sample of measurements needed for atmospheric air quality model development.
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 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.250 |
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; both teacher heads agree on what is shown here.
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".