Bibliographic record
Abstract
We present a novel concept design of a differential absorption LIDAR for open path trace gas sensing in the atmosphere. To perform a range-resolved gas sensing we propose to arrange a set of retroreflectors in the laser beam path to measure a differential absorption in adjacent sections. In validation experiments we used a pulsed DFB quantum cascade laser fabricated by Alpes Lasers. The laser was excited with 200-ns current pulses with a repetition rate of 10 kHz. The frequency chirp rate was found to increase from 7.7 to 1.0 cm-1/μs as peak injection current was increased from 7.1, to 7.8 A. We utilized the frequency chirp at laser substrate temperature of 24.0 °C to scan the 967.0 - 968.5 cm-1 spectral interval containing the absorption lines of CO2 and NH3. We detected ~ 0.25 ppmv of NH3 in nitrogen at atmospheric pressure using a double-pass gas cell with an effective absorption path of 2.4 m. Digital filtering of the spectra was shown to be effective in eliminating a high-frequency noise. To demonstrate range-resolved capabilities of the sensor we used two retroreflectors inserted into the laser beam. A differential absorption of CO2 at 967.7 cm-1 was measured with the gas cell placed in one of the sections. Our experiments indicate that the frequency chirped LIDAR can be used for open path spectroscopy of NH3 over the ranges up to ~ 1 km with a spatial resolution of ~ 30 m and detection limit of ~ 20 ppbv per a 30-m section.
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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) | 0.001 | 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) | 0.002 | 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".