Design of an Optical Fiber-Coupled Sensor for Ambient Methane Measurement
Bibliographic record
Abstract
A tunable diode laser system was developed for remote monitoring of ambient methane using fiber optically connected all-optical sensor heads.Experiments were performed to quantitatively evaluate the influence of selected system hardware and software configurations, with the further goal of enabling measurements of ambient methane concentrations over an intrinsically-safe fiber optic network at sufficient precision and sensitivity to detect unknown fugitive emission sources.The designed system could switch between the sweep integration (SI) and wavelength modulation spectroscopy (WMS) detection methods, which inferred the methane volume mixing ratio from an absorption or second-harmonic (2f) feature respectively.Signals controlling the laser injection current were optimized to balance trade-offs between measurement precision and system sensitivity, and fiber optic components were thermally stabilized to reduce system drift.Starting from this base system, experiments were performed to evaluate the effectiveness of theory-based and experimental calibration methods, software and dual laser approaches to estimating the absorption-free intensity, different signal processing approaches to suppress effects of residual amplitude modulation (RAM) of the laser output intensity, and methods to reduce system drift.Tests also considered effects of varying fiber lengths between the central laser control hardware and the remotely located optical sensor heads.Finally, long-term stability was evaluated by quantifying bias (drift) and precision uncertainty in tests up to 16 months after initial calibration.The best measurement performance was achieved using the WMS method with thermally stabilized optical components within the central control hardware combined with a theory-based calibration, automated daily calibration supported by concurrent softwarebased estimation of the absorption-free intensity, and pair-wise averaging of the 2f feature maxima in each sweep period to suppress effects of RAM.At an averaging time
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".