Standoff detection of natural bioaerosol by range-gated laser-induced fluorescence spectroscopy
Bibliographic record
Abstract
The biological threat has emerged as one of today's primary security challenges due to the increased accessibility to biological warfare technology and the limited efficiency of detection and protection measures against such menace. Defence Research and Development Canada (DRDC) has investigated various methods, including the improvement of atmospheric bioaerosol monitoring, to increase the readiness against such threat. By the end of the 90s, DRDC developed a standoff bioaerosol sensor based on intensified range-gated spectrometric detection of Laser Induced Fluorescence (LIF). This work has showed an important potential of detecting and discriminating in real-time several bioaerosols. The LIDAR system that monitors atmosphere cells from a standoff position induces specific spectrally wide fluorescence signals originating from inelastic interactions with complex molecules forming the building blocks of the bioaerosols. This LIF signal is spectrally collected by a combination of a dispersive element and a range-gated ICCD that records the spectral information within a range-selected atmospheric volume. To assess further the potential of discrimination of such technique, this innovative sensor was used to obtain spectral data of various natural bioaerosols. In order to evaluate the discrimination of biological agent simulants from naturally occurring background fluorescing materials, the obtained results were compared with the ones of bioaerosol simulants (<i>Bacillius subtilis var globiggi </i>(BG) and <i>Erwinia herbicola </i>(EH)) acquired in 2001. The robustness of the spectral data with time was also investigated. From our results, most of the studied natural materials showed a spectral shift of various degrees, and up to 10 nm, to the longer wavelength one year later.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".