Characterization of laser induced fluorescence from background aerosols in a maritime environment
Bibliographic record
Abstract
Defence R&D Canada (DRDC) has developed, by the end of the 90s, a standoff bioaerosol sensor based on intensified range-gated spectrometric detection of Laser Induced Fluorescence (LIF). This sensor called SINBAHD demonstrated the capability to detect and characterize bioaerosols from a stand-off position. The sensor sensitivity and false alarm rate directly depend on the background characteristics since these later will dictate the threshold levels to be used. SINBAHD was used to characterize the background aerosols in a maritime environment close to Halifax, Canada in May 2008. The characterization of the LIF signal from the background aerosols included spectral, temporal and spatial aspects over 8 nights of continuous data collection. The local environmental conditions in addition to the aerosol concentration and particle size distribution were recorded during the entire trial period. From the 64 LIF trials, only five showed specific spectral features. The spectral variability was encountered either at short range, thus closer to the shore, or during a night having a specific prevalent wind direction. Indeed, the detected anomalies were in most cases directly related to the climatic conditions. The integrated LIF signal was also processed to assess the use of LIF intensity to identify aerosol anomalies in a maritime environment.
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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".