Air Quality, Weather, and Visits to the Hospital for Asthma in Northern New England Research Proposal
Bibliographic record
Abstract
The scientific community has identified the human health consequences of climate change and variability as an issue of primary concern. The range of related morbidity and mortality effects include those resulting from extreme heat, storms, floods, vector-borne disease and poor air quality. The relationship of climate and health is complex and presents significant challenges to improving our understanding of relevant causal relationships. The focus of this study is Northern New England, a region that experiences considerable climate variability, both spatially and temporally. The region's air quality is strongly affected by emissions from upwind sources in the Mid-Atlantic, the Midwest, and eastern Canada and by local/regional emissions as well. New England also has a wide variety of landscapes ranging from densely-populated urban areas to largely-forested regions. This proposal is an investigation into the relationship between air quality, weather, and respiratory admissions to the hospital and emergency room. Hospital and emergency room data from several northern New England cities will be gathered and condensed into a daily admission value. This series will be compared with air quality records (ozone, particulate matter, sulfur dioxide, nitrogen dioxide) in search of a relationship. The project will rely upon the efforts of the AIRMAP (Atmospheric Investigations, Regional Modeling, Analysis and Prediction) research program funded by NOAA. The primary mission of AIRMAP is to develop a detailed understanding of climate variability and the source of persistent air pollutants in New England. AIRMAP’s goals include identifying the causes of climate variability, predicting air quality changes as an addition to daily weather forecasts, and demonstrating new forecasting technologies. In addition, this project will serve as a background study for the New England Integrated Sciences and Assessments (NEISA). The NEISA project is seeking to increase understanding of the climate/human health relationship by studying the effects of climate variability and air quality on pulmonary function.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".