Passive standoff detection by differential FTIR radiometry: an overview of the CATSI project with recent results
Bibliographic record
Abstract
A selection of field trial results on the passive standoff detection by differential FTIR radiometry with the CATSI sensor is presented. This selection covers the seven-year development period (1998-2004) of the CATSI project. The results obtained with the CATSI instrument at two major field trials in Kansas (1998) and Nevada (2001) have shown the successful passive standoff detection of a number of chemical vapors at short and medium ranges of 100 m and 1.5 km, respectively. In particular, the detection method has been used at short range (100m) to map the column amounts of a methanol plume with an estimated uncertainty of the order of 15 - 30%. At medium range (1.5 km), the measurement technique has been successfully used to detect and identify low, medium and high concentrations of vapor mixtures of DMMP and SF6 but appears to have limited quantification capabilities in its original form. At long range, CATSI has successfully measured SF6 gas amounts at the 5.7-km range of DRDC Valcartier. The passive standoff detection of liquid contaminants on surfaces was tested with encouraging results. These results indicate that liquid contaminant agents deposited on high-reflectivity surfaces can be detected, identified and possibly quantified with passive sensors. For low-reflectivity surfaces, the presence of contaminants can usually be detected; however, their identification based on simple correlations with the absorption spectrum of the pure contaminant is not possible. In a field trial (Dugway Proving Ground, 2002) on the standoff detection of bio-aerosols, CATSI has detected large amounts of BG at ranges of up to 3 km. Recent field measurements for a standoff distance of 60 m suggests that the gas constituent ratios of complex mixtures can be properly retrieved from passive spectral measurements performed at 8 cm-1. These results from field experiments clearly show the relevance of the CATSI approach for the passive standoff detection by differential FTIR radiometry.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".