A new generation of dual input beam spectroradiometers for the standoff detection of chemicals
Bibliographic record
Abstract
A new generation of field-rated optical subtraction instruments for the standoff detection of chemicals will be presented. It combines the latest ABB Bomem spectroradiometer technology and software with the concepts used in the design of the original instrument designed and manufactured more than 12 years ago by ABB and Defence Research and Development Canada - Valcartier. This instrument is a Fourier-transform spectroradiometer with dual input beams. It is a passive, stand-off sensor that uses one input port to interrogate a scene under investigation while the other input beam can be pointed at the background scene. The instrument automatically measures the difference of spectral radiance between the target and the background scenes by optical subtraction, hence achieving a real-time suppression of the background signal. The resulting measurement is the unique spectral signature of the target measured in real time. The system includes a software module to control the instrument and the acquisition parameters, a module for the radiometric calibration and a module to perform the identification and quantification, in real time, of various gases. An overview of the instrument design and initial results of tests are presented.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".