Standoff sensing of bioaerosols using intensified range-gated spectral analysis of laser-induced fluorescence
Bibliographic record
Abstract
In atmospheric sensing, one application that has demonstrated several impressive successes over the last two decades is the light detection and ranging (LIDAR). Elastic LIDAR has shown an important capability in providing aerosol density and spatial distribution from a standoff position. However, it provides limited information on the material composition of the aerosol component. On the other hand, inelastic LIDARs (including laser-induced fluorescence and Raman LIDARs) measure the spectrally distributed returned signal that may contain important clues about the nature of the scatterers. In order to investigate the capability of these LIDARs in characterizing bioaerosols from a standoff position, Defence Research & Development Canada initiated a three-year program in spring 1999, named SINBAHD (Standoff Integrated Bioaerosol Active Hyperspectral Detection). The aim of the program was to investigate the sensitivity and discrimination capabilities of an inelastic LIDAR based on the intensified range-gated spectral detection of laser-induced fluorescence. An exploratory prototype based on this technique has shown sensitivity of a few living bioaerosol particles per liter of air for a range of 1.4 km at night. Furthermore, based on spectral signatures measured during open-air releases, good discrimination capabilities were obtained between Bacillius subtilis var globiggi (BG) and Erwinia herbicola (EH). These results agree well with a performance model using Raman returns from atmospheric nitrogen as a calibration tool.
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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) | 0.000 | 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 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".