Bibliographic record
Abstract
Abstract. Ground-level ozone (O3) is a secondary air pollutant that has harmful effects on human and ecosystem health. Close to larger bodies of water, the well-known sea- (or lake)-breeze phenomenon plays a role in regulating ground level ozone levels. An observed lake-edge removal effect, where ozone concentration decreases within the first 500 m to 1 km perpendicular to the lake, is thought to be related to the lake-breeze circulation as well as several dilution and removal pathways. A field campaign was conducted in summer 2022 and winter 2023 in two locations on the north shore of Lake Ontario: the urban centre of Toronto, and suburban Oshawa, some 50 km east, to assess how the local environment and season effects the lake-edge removal effect. Ozone, wind speed, and wind direction were measured on 6–7 different days in each season and city along transects perpendicular to Lake Ontario’s shoreline. A consistent negative linear relationship between ozone concentration and distance from shore over the first 500 m (i.e. a lake-edge removal effect) was observed in both cities and both seasons. The ozone gradient changed in Oshawa from -23.5 ± 8.5 (1 standard deviation) ppb/km in summer to -8.1 ± 5.1 ppb/km in winter. The slope remained consistent in Toronto at -15.4 ± 6.7 ppb/km in summer and -16.7 ± 7.3 ppb/km in winter. The year-round observation of an ozone gradient and lake-edge removal effect suggests that factors other than lake-breeze circulation, such as vegetation and titration by NO, have an influence on ozone levels at the lake-land boundary.
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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.026 | 0.022 |
| Insufficient payload (model declined to judge) | 0.202 | 0.173 |
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".