Greenhouse gas emissions from reservoirs of the western United States
Bibliographic record
Abstract
Six reservoirs located in the Western United States (F. D. Roosevelt, Dworshak, Wallula, Shasta, Oroville, and New Melones) were sampled in order to estimate their greenhouse gas (GHG) emissions. Two types of fluxes were assessed: (1) diffusive fluxes of methane (CH4) and carbon dioxide (CO2) at the air/water interface and (2) degassing fluxes of CH4 and CO2 from water passing through the turbine spillways. Diffusive flux measurements indicated that the surface of the reservoirs were a source of CH4 during the sampling period (from +3.2 to +9.5 mg CH4 m−2 d−1). Oroville (+1026 mg CO2 m−2 d−1) and Shasta (+1247 mg CO2 m−2 d−1) surfaces were also sources of CO2. In contrast, the surface of all the other reservoirs constituted sinks for CO2 (from −349 to −1195 mg CO2 m−2 d−1). Degassing fluxes ranged from +0.003 to +0.815 t CH4 d−1, and from +16 to +324 t CO2 d−1. Daily GHG budgets ranged from +0.146 to +2.228 t CH4 d−1, and from −15 to +224 t CO2 d−1. Degassing fluxes represented an important term of these budgets. A significant correlation was observed between the magnitude of CO2 diffusive fluxes and the water pH (R2 = 0.81; p < 0.0001). All other correlations between GHG diffusive fluxes and independent variables tested were weak and/or not significant. Finally, while attempting to resolve the spatial variability in diffusive fluxes, we were able to cluster reservoirs neither according to geological nor ecological criteria.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".