Remote sensing of chemical vapours by differential FTIR radiometry
Bibliographic record
Abstract
This paper presents a novel method for the passive remote sensing of chemical vapours by differential radiometry. The originality of the method lies in the use of a double‐input beam Fourier‐transform infrared (FTIR) spectrometer optimized for optical subtraction. A description of the interferometer (compact atmospheric sounding interferometer (CATSI)) is given along with the detection algorithm (gaseous emission monitoring algorithm (GASEM)) that controls the interferometer data acquisition and performs the on‐line monitoring of chemical vapour parameters. The differential detection method has been successfully tested for several chemical vapours over distances of several hundred metres during open‐air experiments held at Defence Research and Development Canada (DRDC)—Valcartier and Ft Riley, Kansas. In particular, the detection method has been used to map the integrated concentration (column amount) and the temperature of a plume of methanol vapour. In this case, the uncertainties in the methanol plume parameters have been estimated to be of the order of 15–30% for the column amount, and 2–5 K for the gas temperature, which represents an acceptable result for this type of passive remote sensing. The technique has been applied to ammonia vapours and binary mixtures of the two gases as well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".