Bibliographic record
Abstract
Summary of the Relationship Between Indoor and Outdoor Air Pollution and Asthma-Related Morbidity in Asian Countries Indoor and Outdoor Study Design Population (Age in years) Asthma-Related MorbidityCountry and Air PollutionReferenceIndoor Coal use cross-sectional nonsmoking adults (40–69)↓FVC and FEV 1 China [24]Coal use cross-sectional schoolchildren’s fathers↑frequency of wheeze, persistent cough and phlegm, and bronchitis China [27]ETS cross-sectional primary schoolchildren↑frequency of day or night cough, chronic cough, shortness of breath, and bronchitis Taiwan [31]↑frequency of all asthma-like symptoms Israel [32]Active smoke, ETS cross-sectional schoolchildren (11–16)↑frequency of wheezeTaiwan [30]Active smoke cross-sectional schoolchildren’s fathers↑frequency of wheeze, cough, phlegm, persistent cough and phlegm, and bronchitis China [27]Active smoke cross-sectional adults (15)↑frequency of all asthma-like symptoms China [40]Cooking fuels cross-sectional primary schoolchildren↑frequency of asthma*, wheeze, bronchitis, chronic cough, and chronic phlegm China [26]Cooking fuels self-controlled nonsmoking asthmatic women↓PEFRIndia [43]NO
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".