Remote ambient methane monitoring using fiber-optically coupled optical sensors
Bibliographic record
Abstract
A tunable diode laser absorption spectroscopy system, employing a 2 f wavelength modulation spectroscopy measurement scheme, was developed for remote monitoring of ambient methane fluctuations by way of fiber-optically connected all-optical sensors. Detection of fugitive methane emissions demands a measurement precision less than 2.0 ppm v and a lower detection limit less than ~1.7 ppm v methane in air. To determine the optimum base system configuration, the influence of the laser driving signal frequency and amplitude was characterized to strike a balance between measurement precision and system sensitivity. In addition, relative to the basic system configuration, a 50 and 96 % reduction in measurement deviation was achieved by way of polarization scrambling and thermal stabilization of critical optical components, respectively. For methane concentrations between 2.0 and 50.0 ppm v in air, the laboratory-based system achieved a measured precision of 1.36 ppm v and a lower detection limit of 1.56 ppm v using a 6.0-m single-mode optical fiber and an averaging time of 1 s. The long-term system stability and system performance were analyzed using datasets acquired 4 and 12 months after the initial system calibration, yielding a difference in measured precision within the uncertainties of the calibration gas mixture. Finally, it was determined that fiber length between individual remote optical sensors can lead to a varying measurement bias, which implies that length-specific calibrations for each remote optical sensor may be required for a field implementation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".