Evaluation of adaptive algorithms for detection and classification of fluorescent aerosols in the atmosphere
Bibliographic record
Abstract
Photon counting technologies are developed and could be used in the future to measure the return from laser induced fluorescence. Currently, the spectral detection of light emitted by fluorescing aerosols is performed with ICCD, Intensified Charge Coupled Device. The signal to noise ratio of ICCD devices is smaller by a factor of √2compared to photon counting devices having the same sensitivity. We studied the impact of this difference of signal to noise ratio on the capability of multivariate detection and classification algorithms to operate on various conditions. Signal simulations have been performed to obtain ROC (Receiver Operation Characteristics) Curves and Confusion Matrix to obtain the detection performance and the ability of algorithms to discriminate a potential source from another. Two detection algorithms are used, the Integrated Laser Induced Fluorescence(ILIF) and the Matched Filter. For the classification, three algorithms are used, the Adaptive Matched Filter (AMF), the Adaptive Coherent Estimator (ACE) and the Adaptive Least Squares (ALS). The best algorithm for detection is the AMF using the signature of the material present in a cloud, the ILIF detector performs very well. For the classification, the three algorithms are surprisingly giving the same results for the same data. The classification performs better if the distance between the signatures recorded in a database is important. The performance of the detector and of the classificator improves with an increase of the signal to noise ratio and is consistently and significantly better for the photon counting compared to ICCD.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".