SPECTRAL PROCESSING OF LASER-INDUCED FLUORESCENCE FROM THREATENING BIOLOGICAL AEROSOLS
Why this work is in the frame
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Bibliographic record
Abstract
The stand-off detection classification by laser induced fluorescence is the objective of the Biosense project. The sensor will perform the monitoring of a defined area around its location using an elastic lidar detector for particles cloud. The detection of cloud will trigger fluorescence probing of the cloud. To perform this task the area fluorescence background will be monitored in order to evaluate if a return signal changed. Using a simple signal model built with experimental data, we designed a detection and monitoring procedure for the fluorescence at a single location. Signal simulations have been performed to verify the operation of the system. The results of the simulation indicate the system is able to detect anomaly with small contrast between a signal and the background. The results will have to be extended to area surveillance and a more complete signal model for various environments in natural conditions is required
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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 | 0.000 | 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 it