Simulation of ultraviolet laser-induced fluorescence LIDAR for detecting bioaerosol
Bibliographic record
Abstract
The biological warfare agent (BWA) is a kind of terrible threat during the war or raid from the terrorist. Last decade, the interest in utilizing ultraviolet laser-induced fluorescence (UV-LIF) LIDAR to detect the bioaerosol cloud has risen in order to measure the distribution of the bioaerosol particle. The UV-LIF LIDAR system can remotely detect and classify the bioaerosol agents and it is an active detecting system. As the infrared absorbing in the atmosphere is less, the range of infrared remote sensing is very far. The infrared laser at 1064 nm wavelength firstly begins to work in the UV-LIF LIDAR system and the aerosol cloud can be detected at very long range through the elastic backscattering signal from aerosol irradiated by infrared laser. But the category of aerosol can't be identified yet. If the infrared elastic backscattering level exceeds a threshold, UV laser at 355 nm wavelength will be triggered and induce the fluorescence. The excitated spectra of fluorescence can be used for discrimination of different aerosol species and particle concentration. This paper put forward for a UV-LIF LIDAR system model and the principle of the model is described summarily. Then the system parameters are presented and the simulation and analysis of the infrared elastic backscattering and laser-induced fluorescence are made, which is based on these parameters. Raman backscattering signal of Nitrogen gas in the atmosphere generally is taken to reduce measuring error, so the article also simulates this Raman backscatter signal at 387 nm wavelength. The studies above may provide some valuable instructions to the design of a real UV-LIF LIDAR system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".