Hyperspectral fluorescence lifetime lidar for geological exploration
Bibliographic record
Abstract
We have developed a small, relatively lightweight and efficient lidar instrument for remotely detecting and classifying minerals. The system is based on a pulsed, eye-safe, diode pumped Nd:YAG laser, tripled (355nm) or quadrupled (266nm), for UV excitation of minerals, which then fluoresce with a typical spectrum and lifetime. Fluorescence is detected through a telescope / filter / fiber bundle / spectrograph / multi-channel detector system capable of photon counting. Transmission and detection efficiency have been optimized to reduce the need for high optical excitation energy. Detection electronics are based on gated charge integration using a multi-anode photomultiplier tube. Spectra shown are measured in the 420 to 720 nm visible range with 355 nm laser excitation. Results show that it is relatively easy to distinguish between vegetation and non-vegetation spectra using lifetime data. Lifetime of vegetation is relatively short when compared to the mineral samples investigated. Although results shown are measured in a controlled environment on the ground, the system is being developed for eventual use in a low altitude airborne application. System parameters are presented and upgrade paths are discussed.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".