Spatial Dependence of Gas Transfer Velocity in Boreal Reservoirs
Bibliographic record
Abstract
Abstract Reservoirs are significant sources of greenhouse gases (GHG), but the magnitude of these emissions remains uncertain. The key to deriving robust diffusive fluxes is combining accurate surface water gas concentrations with realistic gas transfer velocities ( k 600 ). Existing models, primarily based on wind speed, estimate system‐wide k 600 values and typically do not transpose well between systems, particularly for reservoirs with complex shapes. This study aims to improve current models to estimate k 600 in such reservoirs. We focus on the La Romaine complex, composed of three cascading reservoirs (RO1, RO2, RO3) in northeastern Canada. We measured 535 carbon dioxide (CO 2 ) and methane (CH 4 ) fluxes using floating chambers during 11 field campaigns and calculated k 600 from these fluxes. Besides wind speed, within‐reservoir location related to depth and distance to shore strongly modulated k 600, but this location effect was reservoir‐dependent. Based on these three variables, we generated reservoir‐specific spatially explicit k 600 models that account for the heterogeneity in fetch within each reservoir. For RO1, our generated model explained up to 110% more of the variance in measured k 600 compared to any published system‐wide k 600 model to date, and for RO2 and RO3, up to 46% and 28%, respectively. We show that the same modeling framework can be applied to other reservoirs with complex shapes. Understanding how location within the reservoir modulates the influence of wind on k 600 will help constrain current reservoir GHG fluxes uncertainties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".