Characterizing a Quantum Cascade Tunable Infrared Laser Differential Absorption Spectrometer (QC-TILDAS) for measurements of atmospheric ammonia
Bibliographic record
Abstract
Abstract. A compact, fast-response Quantum Cascade Tunable Infrared Laser Differential Absorption Spectrometer (QC-TILDAS) for measurements of ammonia (NH3) has been evaluated under both laboratory and field conditions. Absorption of radiation from a pulsed, thermoelectrically cooled QC laser occurs at reduced pressure in a 0.5 L multiple pass absorption cell with an effective path length of 76 m. Detection is achieved using a thermoelectrically-cooled Mercury Cadmium Telluride (HgCdTe) infrared detector. A novel sampling inlet was used, consisting of a short, heated, quartz tube with a hydrophobic coating to minimize the adsorption of NH3 to surfaces. The inlet contains a critical orifice that reduces the pressure, a virtual impactor for separation of particles, and additional ports for delivering NH3-free background air and calibration gas standards. The level of noise in this instrument has been found to be 0.23 ppb at 1 Hz. The sampling technique has been compared to the results of a conventional lead salt Tunable Diode Laser Absorption Spectrometer (TDLAS) during a laboratory intercomparison. The effect of humidity and heat on the surface interaction of NH3 with sample tubing was investigated at mixing ratios ranging from 30–1000 ppb. Humidity was seen to worsen the NH3 time response and considerable improvement was observed when using a heated sampling line. A field intercomparison of the QC-TILDAS with a modified Thermo 42CTL chemiluminescence-based analyzer was also performed at Environment Canada's Centre for Atmospheric Research Experiments (CARE) in the rural town of Egbert, ON between May–July 2008. Background tests and calibrations using two different permeation tube sources and an NH3 gas cylinder were regularly carried out throughout the study. Results indicate a very good correlation at 1 min time resolution (R2 = 0.93) between the two instruments at the beginning of the study, when regular background subtraction was applied to the QC-TILDAS. An overall good correlation of R2 = 0.85 was obtained over the entire two month data set, where the majority of the spread can be attributed to differences in inlet design and background subtraction methods.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".