Spectrally resolved laser-induced fluorescence for bioaerosols standoff detection
Bibliographic record
Abstract
An efficient standoff biological warfare detection capability could become an important asset for both defence and security communities based on the increasing biological threat and the limits of the presently existing protection systems. Defence R&D Canada (DRDC) has developed, by the end of the 90s, a standoff bioaerosol sensor prototype based on intensified range-gated spectrometric detection of Laser Induced Fluorescence (LIF). This LIDAR system named SINBAHD monitors the spectrally resolved LIF originating from inelastic interactions with bioaerosols present in atmospheric cells customizable in size and in range. SINBAHD has demonstrated the capability of near real-time detection and classification of bioaerosolized threats at multi-kilometre ranges. In spring 2005, DRDC has initiated the BioSense demonstration project, which combines the SINBAHD technology with a geo-referenced Near InfraRed (NIR) LIDAR cloud mapper. SINBAHD is now being used to acquire more signatures to add in the spectral library and also to optimize and test the new BioSense algorithm strategy. In September 2006, SINBAHD has participated in a two-week trial held at DRDC-Suffield where different open-air wet releases of live and killed bioagent simulants, growth media and obscurants were performed. An autoclave killing procedure was performed on two biological materials (Bacillus subtilis var globigii or BG, and Bacillus thuringiensis or Bt) before being aerosolized, disseminated and spectrally characterized with SINBAHD. The obtained results showed no significant impact of this killing process on their normalised spectral signature in comparison with their live counterparts. Correlation between the detection signals from SINBAHD, an array of slit samplers and a FLuorescent Aerosol Particle Sensor (C-FLAPS) was obtained and SINBAHD's sensitivity could then be estimated. At the 2006 trial, a detection limit of a few tens of Agent Containing Particles per Liter of Air (ACPLA) was obtained for a 15-m thick cloud of live BG located at a range of 400 m.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".