Improving long-term air pollution estimates with incomplete data: A method-fusion approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mobile air pollution monitoring is an effective means of collecting spatially and temporally diverse air pollution samples. These observations are often used to predict long-term air pollution concentrations using temporal adjustments based on the time-series of a fixed location monitor. Temporal adjustments are required because the time-series is often incomplete at each spatial location. We describe a method-fusion temporal adjustment that has been demonstrated to improve the accuracy of long-term estimates from incomplete time-series data. Our adjustment approach combines the techniques of using a log transformation to modify the air pollution samples to a near normal distribution and incorporates the long-term median of a reference monitor to mediate the effects of estimate inflation created by outliers in the data. We demonstrate the approach with hourly Nitrogen Dioxide observations from Paris, France in 2016. Method-Fusion Benefits: •Log transformations control for estimate inflation created by log normally distributed data.•Adjusting data with the long-term median, rather than the mean, controls for estimate inflation.•Produces more accurate long-term estimates than other adjustments independent of the pollutant being estimated.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it