PTR-QMS versus PTR-TOF comparison in a region with oil and natural gas extraction industry in the Uintah Basin in 2013
Bibliographic record
Abstract
Abstract. Here we compare volatile organic compound (VOC) measurements using a standard proton-transfer-reaction quadrupole mass spectrometer (PTR-QMS) with a new proton-transfer-reaction time of flight mass spectrometer (PTR-TOF) during the Uintah Basin Winter Ozone Study 2013 (UBWOS2013) field experiment in an oil and gas field in the Uintah Basin, Utah. The PTR-QMS uses a quadrupole, which is a mass filter that lets one mass to charge ratio pass at a time, whereas the PTR-TOF uses a time of flight mass spectrometer, which takes full mass spectra with typical 0.1 s–1 min integrated acquisition times. The sensitivity of the PTR-QMS in units of counts per ppbv (parts per billion by volume) is about a factor of 10–35 times larger than the PTR-TOF, when only one VOC is measured. The sensitivity of the PTR-TOF is mass dependent because of the mass discrimination caused by the sampling duty cycle in the orthogonal-acceleration region of the TOF. For example, the PTR-QMS on mass 33 (methanol) is 35 times more sensitive than the PTR-TOF and for masses above 120 amu less than 10 times more. If more than 10–35 compounds are measured with PTR-QMS, the sampling time per ion decreases and the PTR-TOF has higher signals per unit measuring time for most masses. For UBWOS2013 the PTR-QMS measured 34 masses in 37 s and on that timescale the PTR-TOF is more sensitive for all masses. The high mass resolution of the TOF allows for the measurements of compounds that cannot be separately detected with the PTR-QMS, such as oxidation products from alkanes and cycloalkanes emitted by oil and gas extraction. PTR-TOF masses do not have to be preselected, allowing for identification of unanticipated compounds. The measured mixing ratios of the two instruments agreed very well (R2 ≥ 0.92 and within 20%) for all compounds and masses monitored with the PTR-QMS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".