Segmented flow coil equilibrator coupled to a Proton Transfer ReactionMass Spectrometer for measurements of a broad range of VolatileOrganic Compounds in seawater
Bibliographic record
Abstract
Abstract. Here we present a technique that utilises a segmented flow coil equilibrator coupled to a Proton Transfer Reaction-Mass Spectrometer to measure a broad range of dissolved organic gases. Due to its unique design composed of a segmented flow and a headspace-water separator, the equilibrator is highly efficient for gas exchange and has a fast response time (under 1 min). The system allows for both discrete and continuous measurements of volatile organic compounds in seawater due to its ease of changing sample intake and low sample water flow (100 cm3 min-1). The equilibrator setup is both relatively inexpensive and compact. Hence it can be easily reproduced and installed on a variety of oceanic platforms, particularly where space is limited. As a result of its smooth and unreactive surfaces, the segmented flow coil equilibrator is expected to be less sensitive to biofouling and easier to clean than membrane-based equilibration systems. The equilibrator fully equilibrates for gases that are similarly soluble or more soluble than toluene. The method has been successfully deployed in the Canadian Arctic. Here, some example data of underway surface water and Niskin bottle measurements in the sea ice zone are presented to illustrate the efficacy of this measurement system.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".