The "Lung": a software-controlled air accumulator for quasi-continuous multi-point measurement of agricultural greenhouse gases
Bibliographic record
Abstract
Abstract. We describe the design and testing of a flexible bag ("Lung") accumulator attached to a gas chromatographic (GC) analyzer capable of measuring surface-atmosphere greenhouse gas exchange fluxes in a wide range of environmental/agricultural settings. In the design presented here, the Lung can collect up to three gas samples concurrently, each accumulated into a Tedlar bag over a period of 20 min or longer. Toggling collection between 2 sets of 3 bags enables quasi-continuous collection with sequential analysis and discarding of sample residues. The Lung thus provides a flexible "front end" collection system for interfacing to a GC or alternative analyzer and has been used in 2 main types of application. Firstly, it has been applied to micrometeorological assessment of paddock-scale N2O fluxes, discussed here. Secondly, it has been used for the automation of concurrent emission assessment from three sheep housed in metabolic crates with gas tracer addition and sampling multiplexed to a single GC. The Lung allows the same GC equipment used in laboratory discrete sample analysis to be deployed for continuous field measurement. Continuity of measurement enables spatially-averaged N2O fluxes in particular to be determined with greater accuracy, given the highly heterogeneous and episodic nature of N2O emissions. We present a detailed evaluation of the micrometeorological flux estimation alongside an independent tuneable diode laser system, reporting excellent agreement between flux estimates based on downwind vertical concentration differences. Whilst the current design is based around triplet bag sets, the basic design could be scaled up to a larger number of inlets or bags and less frequent analysis (longer accumulation times) where a greater number of sampling points are required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".