Recent advances in the passive standoff detection of chemical vapors by differential FTIR radiometry
Bibliographic record
Abstract
A method for the passive remote monitoring of chemical vapours by differential Fourier-transform infrared (FTIR) radiometry is presented for determining the characteristics of chemical vapour plumes released from stacks at various horizontal distances from the FTIR sensor. The measurement technique is based on the use of a double-beam FTIR spectrometer that is optimized for optical subtraction. A description is given of the customized interferometer, along with a discussion of the analysis algorithm that has been developed for the on-line detection, identification and quantification of chemical vapour plumes. Experimental results are presented from a number of open-air trials that demonstrate the passive detection, identification and quantification of vapour plumes, which consist of DMMP, ammonia, methanol and sulphur hexafluoride gases probed at standoff distances of up to 1.5 km. In addition, recent results obtained at the open-air range at Defence Research and Development Canada (DRDC)-Valcartier are presented for the detection of a vapour plume of sulphur hexafluoride (SF6) measured at a standoff distance of 5.7 km. This work represents the first such measurement reported in the open literature for such a large standoff distance. These results clearly demonstrate the applicability of the differential radiometry approach for successfully detecting and identifying chemical vapour clouds located at long distances from the sensor.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".