Role of turbines in the carbon dioxide emissions from two boreal reservoirs, Québec, Canada
Bibliographic record
Abstract
The potential for degassing of carbon dioxide stemming from the passage of water through turbines of hydroelectric reservoirs was studied in two boreal reservoirs (La Grande 2 and La Grande 3) located in the James Bay region of Québec, Canada. Samples of dissolved CO2 were taken monthly over a period of 1 year from the main reservoirs, within the hydroelectric facilities from the shaft entering the turbine system and from the exits below the facilities. Diffusive fluxes from the reservoir surfaces were calculated using the thin boundary layer equation. The differences between CO2 concentrations above and below the dams were used to calculate the amount of degassing per unit of water turbined. Diffusive flux calculations indicated that the reservoirs acted as sources of CO2 to the atmosphere throughout the sampling period, with fluxes ranging between 80 and 1800 mg CO2 m−2 d−1 at LG2 and between 400 and 1500 mg CO2 m−2 d−1 at LG3. Degassing calculated from turbining ranged between 5–45 and 5–25 t d−1 at LG2 and LG3, respectively, and represented between <1 and 7% and mean weighted values of <1% of the equivalent fluxes across the air‐water interface of the main reservoirs. The quantity of degassing is seasonally defined, with highest rates observed in the winter/spring period, a result of lower water temperature effects on the solubility of CO2, and the buildup of gases over the winter period due to mineralization of organic matter and the influx from watershed sources due to the springtime melt. Depending on the effluxes occurring at the air‐water interface of the main reservoir, degassing can represent a maximum equivalent 16%. This study indicates that the main role of turbining lies in the seasonality of release of GHG rather than the absolute amount.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".