Bioaerosols laser-induced fluorescence provides specific robust signatures for standoff detection
Bibliographic record
Abstract
One of today's primary security challenges is the emerging biological threat due to the increased accessibility to biological warfare technology and the limited efficiency of detection against such menace. At the end of the 90s, Defence R&D Canada developed a standoff bioaerosol sensor, SINBAHD, based on intensified range-gated spectrometric detection of Laser Induced Fluorescence (LIF) with an excitation at 351 nm. This LIDAR system generates specific spectrally wide fluorescence signals originating from inelastic interactions with complex molecules forming the building blocks of most bioaerosols. This LIF signal is spectrally collected by a combination of a dispersive element and a range-gated ICCD that limits the spectral information within a selected atmospheric cell. The system can detect and classify bioaerosols in real-time, with the help of a data exploitation process based on a least-square fit of the acquired fluorescence signal by a linear combination of normalized spectral signatures. The detection and classification processes are hence directly dependant on the accuracy of these signatures to represent the intrinsic fluorescence of bioaerosols and their discrepancy. Comparisons of spectral signatures acquired at Suffield in 2001 and at Dugway in 2005 of bioaerosol simulants, Bacillius subtilis var globiggi (BG) and Erwinia herbicola (EH), having different origin, preparation protocol and/or dissemination modes, has been made and demonstrates the robustness of the obtained spectral signatures in these particular cases. Specific spectral signatures and their minimum detectable concentrations for different simulants/interferents obtained at the Joint Biological Standoff Detection System (JBSDS) increment II field demonstration trial, Dugway Proving Ground (DPG) in June 2005, are also 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 | 0.000 | 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".