Bibliographic record
Abstract
High levels of ambient oxidant air pollution, such as ozone, have been associated with negative health effects in elite athletes. The photochemical model OzCalc was used to calculate ozone concentrations as a function of time in Beijing for the 2008 Olympic Summer Games. By making use of the data obtained in Hastings, Ontario in August 1993, the model was adjusted for Beijing with respect to radiation geometry and ambient levels of primary air pollutants. Ozone concentrations were calculated using three scenarios: keeping pollution levels constant, a moderate success in pollution clean-up, and a high success in pollution clean-up. Results of ozone obtained with the model predicted very high levels associated with Scenario’s 1 and 2, while Scenario 3 showed acceptable levels for athletic competition, in accordance with WHO and Chinese Standards. A high NO-to-NO2 concentration ratio showed less ozone formation, which displayed the dependence of ozone on the variable levels of NO2 and NO. The level of the hydroxyl radical formation was closely related to that of ozone. The overall results suggest that Beijing will have a difficult time bringing pollution down to an acceptable level; athletes will have to deal with an added negative physiological response.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".