A novel high-resolution in situ tool for studying biogeochemical processes in aquatic systems: The Lake Aiguebelette case study
Bibliographic record
Abstract
Inland waters are a significant source of atmospheric methane (CH4), a greenhouse gas (GHG) 34-85 times stronger than carbon dioxide (on 100 to 20-yr timescales) and responsible for ~23% of global radiative forcing. Of the GHGs produced by inland waters (i.e., carbon dioxide, CH4 and nitrous oxide), CH4 is responsible for ~75% of the climatic impact of aquatic GHG emissions with aquatic CH4 emissions comparable to the largest global CH4 emitters - wetlands and agriculture. Considering that aquatic systems contribute up to half of global CH4 emissions and that CH4 is predominantly formed in anoxic environments such as lake sediments, the source and quantification of ubiquitous surface CH4 observed in most aquatic systems are a question of global importance.In this work we present the first deployment of a novel membrane inlet laser spectrometer (MILS) instrument, composed of a mid-infrared spectrometer for simultaneous detection of CH4, C2H6 and d13CH4 coupled with a fast response (t90 < 30sec) membrane extraction system. During a 1-day field campaign, we performed a 2D mapping of dissolved CH4, C2H6 and d13CH4 of surface water of Lake Aiguebelette (France) highlighting the advantages of continuous high-resolution mapping of dissolved gases.The results showed the presence of CH4 sources less enriched in 13C in the littoral zone (presumably the littoral anoxic sediments). The CH4 pool became more enriched in 13C with distance from shore, suggesting that oxidation processes prevailed over epilimnetic CH4 production. The data obtained were in line with recent multi-lake studies.
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 | 0.000 | 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".