Bioaerosols standoff detection simultaneously refereed with particle concentration (ppl) and viability units (ACPLA)
Bibliographic record
Abstract
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) called SINBAHD. This LIDAR system was used to characterize spectrally the LIF of bioaerosol agent simulants and obscurants during 57 cross-wind open-air releases at Suffield, CAN in July 2007. An autoclave and gamma-irradiation killing procedures were performed on Bacillus subtilis var globigii (BG) samples before they were aerosolized, disseminated and spectrally characterized. Slight discrepancies were observed in the spectral characteristics of killed versus live samples but none between the two killing methodologies. Significant signature variabilities were observed from the different batches of Erwinia Herbicolas (EH). The generated cloud was simultaneously characterized in Agent Containing Particle per Liter of Air (ACPLA) by slit sampler units and in particle per litter of air (ppl) by an Aerodynamic Particle Sizer (APS). Correlation assessment between the stand-off sensor SINBAHD and the two referee point sensors was done, allowing an estimation of SINBAHD's sensitivity in ACPLA and in ppl. For a 20-m thick cloud at a range of 990 m, a detection limit of a few tens of ACPLA and a few ACPLA were obtained for BG and EH respectively. The extracted correlation between ACPLA and ppl data for releases performed with an agricultural sprayer showed a high degree of variability: 2 to 29% and 1 to 6% of ACPLA/ppl ratio for BG and EH, respectively.
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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 | 0.001 |
| 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 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".