Latest developments in active remote sensing at INO
Bibliographic record
Abstract
Remote sensing or stand-off detection using controlled light sources is a well known and often used technique for atmospheric and surface spatial mapping. Today, ground based, vehicle-borne and airborne systems are able to cover large areas with high accuracy and good reliability. This kind of detection based on LiDAR (Light Detection and Ranging) or active Differential Optical Absorption Spectroscopy (DOAS) technologies, measures optical responses from controlled illumination of targets. Properties that can be recorded include volume back-scattering, surface reflectivity, molecular absorption, induced fluorescence and Raman scattering. The various elastic and inelastic backscattering responses allow the identification or characterization of content of the target volumes or surfaces. INO has developed instrumentations to measure distance to solid targets and monitor particles suspended in the air or in water in real time. Our full waveform LiDAR system is designed for use in numerous applications in environmental or process monitoring such as dust detection systems, aerosol (pesticide) drift monitoring, liquid level sensing or underwater bathymetric LiDARs. Our gated imaging developments are used as aids in visibility enhancement or in remote sensing spectroscopy. Furthermore, when coupled with a spectrograph having a large number of channels, the technique becomes active multispectral/hyperspectral detection or imaging allowing measurement of ultra-violet laser induced fluorescence (UV LIF), time resolved fluorescence (in the ns to ms range) as well as gated Raman spectroscopy. These latter techniques make possible the stand-off detection of bio-aerosols, drugs, explosives as well as the identification of mineral content for geological survey. This paper reviews the latest technology developments in active remote sensing at INO and presents on-going projects conducted to address future applications in environmental monitoring.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.016 |
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".