An automated, high through‐put method for accurate and precise measurements of dissolved nitrous‐oxide and methane concentrations in natural waters
Bibliographic record
Abstract
Abstract We describe a technique for measuring dissolved CH4 and N2O concentrations from discrete water samples using an automated purge and trap gas extraction system, coupled with a gas chromatograph‐mass spectrometer (PT‐GCMS). The automated system measures blanks, standards, and 25 samples in less than five hours with only ∼ 30 min of operator involvement. Rigorous testing of the PT‐GCMS demonstrates sensitivity, accuracy and precision that is comparable or better than conventional methods for CH4 and N2O analysis. Measured concentrations of CH4 and N2O in air‐equilibrated water samples showed good agreement with expected values derived from solubility calculations, and results of a multilaboratory intercalibration exercise showed that our measurements agree with those made using conventional methods. Precision of replicate water samples is 3.3% for CH4 and 3.0% for N2O. Detection limits are well below the expected concentrations in most natural waters with a five milliliters sample, and can be lowered substantially by analyzing a larger sample volume. To demonstrate the utility of the method, we present depth profiles of CH4 and N2O from Saanich Inlet, a coastal anoxic fjord in British Columbia. The Saanich Inlet water column exhibits rapid changes in CH4 and N2O across depth‐dependent and seasonally variable redox conditions. Our high through‐put, automated method facilitates the measurement of aqueous N2O and CH4 concentrations, and will thus help to improve our understanding of the natural cycling of these climate‐active trace gases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".