A Novel High‐Resolution In Situ Tool for Studying Carbon Biogeochemical Processes in Aquatic Systems: The Lake Aiguebelette Case Study
Bibliographic record
Abstract
Abstract Lakes and reservoirs are a significant source of atmospheric methane (CH 4 ), with emissions comparable to the largest global CH 4 emitters. Understanding the processes leading to such significant emissions from aquatic systems is therefore of primary importance for producing accurate projections of emissions in a changing climate. In this work, we present the first deployment of a novel membrane inlet laser spectrometer (MILS) for fast simultaneous detection of dissolved CH 4 , ethane (C 2 H 6 ) and the stable carbon isotope of methane (δ 13 CH 4 ). During a 1‐day field campaign, we performed 2D mapping of surface water of Lake Aiguebelette (France). Average dissolved CH 4 concentrations and δ 13 CH 4 were 391.9 ± 156.3 nmol L −1 and −67.3 ± 3.4‰ in the littoral area and 169.8 ± 26.6 nmol L −1 and −61.5 ± 3.6‰ in the pelagic area. The dissolved CH 4 concentration in the pelagic zone was 50 times larger than the concentration expected at equilibrium with the atmosphere, confirming an oversaturation of dissolved CH 4 in surface waters over shallow and deep areas. The results suggest the presence of CH 4 sources less enriched in 13 C in the littoral zone (presumably the littoral sediments). The CH 4 pool became more enriched in 13 C with distance from shore, suggesting that oxidation prevailed over epilimnetic CH 4 production and it was further confirmed by an isotopic mass balance technique with the high‐resolution data. This new in situ fast response sensor allows one to obtain unique high‐resolution and high‐spatial coverage data sets within a limited amount of survey time. This tool will be useful in the future for studying processes governing CH 4 dynamics in aquatic systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".