<title>New technique for remote sensing of air pollution in the lower troposphere</title>
Bibliographic record
Abstract
A new technique for measuring air pollutants in the lower part of the troposphere has been developed. The technique is based on Fourier-transform infrared (FTIR) spectroscopy, which is used to measure the atmospheric thermal emission from gases beneath uniform cloud cover. The cloud acts as a cold background emission source against which the emission from gases in the warmer atmosphere beneath the cloud may be detected. The region of the infrared spectrum near 2400 cm-1, which is nearly void of significant atmospheric water vapour emission, is used to infer the cloud base temperature. The FASCOD3 atmospheric transmission code is used to simulate the background emission spectrum below the cloud, which is then subtracted from the measured spectrum to yield the thermal emission band of a particular gas. Based on the band intensity, the average concentration of the gas in the lower atmosphere may be determined. In order to have sufficient detection sensitivity, the cloud base must exceed an altitude of about 1 km. The gases that have been successfully measured with this technique include tropospheric ozone, carbon dioxide, carbon monoxide and nitrous oxide. By comparing the tropospheric ozone amounts to the surface amounts measured with an ozone analyser over the past two summers, it was discovered that the ozone residing in the lower troposphere sometimes has a concentration that is nearly twice the value recorded at the surface. This result has important implications concerning air pollution models, which normally incorporate ozone amounts from meteorological stations at the surface.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.014 |
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".