Air Sensor Network Analysis Tool: R-Shiny Application
Bibliographic record
Abstract
Poor air quality can harm human health and the environment. Air quality data is needed to understand and reduce exposure to air pollution. Air sensor data can sup-plement national air monitoring network data to better understand localized air qual-ity and trends. However, these sensors can have limitations, biases, and inaccuracies that must first be controlled to generate data of adequate quality. Analyzing sensor data requires not only a background knowledge of air quality but also often requires extensive data analysis skills which may require new skills or time that burdens many air agencies (e.g., small states, local). To address these issues, an R-Shiny application has been developed to assist air quality professionals in 1) understanding air sensor data quality through comparison with nearby ambient air reference monitors, 2) ap-plying basic quality assurance (QA) and quality control (QC), and 3) understanding local air quality conditions. This tool provides agencies with the ability to more quickly analyze and utilize air sensor data for a variety of purposes while increasing the re-producibility of analyses. This paper highlights a case study using the tool to explore sensor performance during Canadian wildfire smoke impacts in the midwestern United States during June of 2023.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".