Physical Activity, a Critical Exposure Factor of Environmental Pollution in Children and Adolescents Health Risk Assessment
Bibliographic record
Abstract
It is an extremely urgent problem that physical fitness promotion must face not only the increasing air pollution but also the decline of physical activity level of children and adolescents worldwide at present, which is the major reason that forms an inactive lifestyle and does harm to adolescents' health. Thus, it is necessary to focus on the exposure factor in environmental health risk assessment (EHRA) which conducts supervision of environmental pollution and survey of adolescents' activity patterns according to the harmful characteristics of air pollutant and relationship between dose and response. Some countries, such as USA, Canada and Australia, regard both respiratory rate and physical activity pattern as main exposure factors for adolescents in both air pollution health risk assessment and exercise risk assessment to forecast a safe exposing condition of pollutant for adolescents while they are doing exercise outdoors. In addition, it suggests that the testing indexes and testing methods of these two exposure factors, such as investigating the time of daily physical activity, strength, and characteristic of frequency, help to set up the quantitative relationship between environmental pollution index and the time, strength, frequency of daily activities, and formulate children's and adolescents' activity instructions under different levels of environmental pollutions. As smog becomes increasingly serious at present, it is meaningful to take physical activity as a critical composition of exposure factor and establish physical activity guideline, so as to reduce the risk of air pollution, and promote physical health of children and adolescents effectively.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".