Improving long-term air pollution estimates with incomplete data: A method-fusion approach
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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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".