Bнутригодовая динамика загрязнения атмосферного воздуха и обращаемости за медицинской помощью по поводу болезней органов дыхания
Bibliographic record
Abstract
To study seasonal variation in air pollution and its associations with hospital admissions for respiratory diseases in Novodvinsk. Methods: Average monthly counts for hospital admission for respiratory diseases as well as average monthly concentrations of air pollutants for the period 2001-2008 were analyzed. Associations between daily concentrations of each of the five pollutants and daily counts of hospital admissions were assessed using time series analysis. Autoregressive Poisson models with corrections for overdispersion, long-term, seasonal and weekly variations were applied and 95 % confidence intervals for all coefficients were calculated. Results: The level of hospital admission for respiratory diseases from September to December, in January and April was higher than average annual level by 115-118 %, while in February and March it was only 40 % and 27 % of the average for 8 years, respectively. Concentrations of particulate matters, carbon monoxide, sulfur dioxide (SO2), nitrogen dioxide (NO2) and hydrogen sulfide were higher during warm season. Daily concentrations of SO2 and NO2 were positively associated with daily counts of hospital admission for respiratory diseases while inverse association was observed for CO.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".