New concept design of differential absorption lidar: fusion of DIAL and TDLS methods
Bibliographic record
Abstract
We propose a new approach to range-resolved remote gas sensing in the atmosphere based on a combination of a DIAL and tunable-laser diode spectroscopy (TDLS) methods. To add range-resolving capabilities to a TDLS sensor we propose to arrange a group of retroreflectors (RRs) dividing an absorption path into adjacent measurement sections similar to those utilized by conventional DIAL systems. We implemented two techniques for the interrogation of the RRs: 1) scanning a beam of a continuous-wave laser over RRs sequentially; 2) using a time delay between returns from different RRs illuminated with a pulsed laser. We employed scanning technique with a vertical-cavity surface-emitting laser (VCSEL) operating near 1389 nm. A single-pulse interrogation method was demonstrated with a 10.9-&mgr;m quantum cascade laser (QCL) suitable for detection of ammonia, ethylene and water vapor in the atmosphere. Gas sensing and ranging was performed over distances varying from ~ 1 m up to ~ 1 km. Using VCSEL we attained a 0.5-s time resolution in gas concentration profiling with a 10-cm spatial resolution. Minimum interrogation time of a group of RRs was ~ 9 ms. A new generation of differential absorption LIDARs can be developed for range-resolved gas sensing in the atmosphere over distances up to ~ 1 km. The instruments can be used for a variety of applications ranging from fencing industrial areas to monitor fluxes of atmospheric pollutants to continuous air quality control in populated areas
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".