Automated CO2 and CH4 monitoring system for continuous estimation of degassing related to hydropower
Bibliographic record
Abstract
Reliable measurement of greenhouse gas emissions from reservoirs is essential for estimating the carbon footprint of the hydropower industry. Among the different emission pathways, degassing downstream of the turbines and spillway is poorly documented mainly because of the safety stakes related to sampling up and downstream the power plants. The alternative being to sample the water from the turbine inside the station, this study aimed to assemble a custom automated CO 2 and CH 4 monitoring system (SAGES), especially designed for long-term surveys in hydropower facilities, with a special focus on low maintenance requirements. The SAGES combines infrared and laser technologies with a modular programming approach and run with a specifically designed plexiglass equilibration system (PES) that maintain a permanent headspace and avoid clogging by suspended solids. Although the SAGES is based on commercially available devices, it is the first time they are combined and used with the gas equilibrator. To ensure the reliability of the mounting and to control the quality of the readings, the system was tested in laboratory prior to its installation in generating stations. SAGES and PES performances were compared with those of generic devices available on the market although less adapted to the specific deployments targeted. The SAGES gas partial pressure measurements were accurate and linear in the entire range tested: 0 to 5,000 ppm for p CO 2 and 0 to 600 and 10,000 ppm for p CH 4 . Gas PP measurements were comparable to the reference CO 2 /CH 4 sensor and there was no drift during long term deployment. The SAGES/PES installed in 2021 in cascading generating stations of the Romaine complex collected more than 28,000 data points over a 10-month period and required only two maintenances. Results show that the SAGES is a reliable tool that provide long-term CO 2 and CH 4 dataset in generating stations while requiring minimal energy, care and maintenance. The data collected in turbine water and the recent use of the SAGES in peat land by a collaborative team demonstrate how the SAGES systems can efficiently contribute to the understanding of reservoir carbon cycles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".