Interplay between convection and wind in driving surface mixing in the Xiangjiaba Reservoir, China
Bibliographic record
Abstract
Near-surface processes in lakes and reservoirs strongly influence basin-scale circulation, mixing, and ecosystem functioning. While wind- and convection-driven mixing are recognized in lakes, their individual and interactive contributions in morphologically complex reservoirs remain insufficiently quantified. Using high-frequency temperatures in a large reservoir, we identified three mixing regimes during autumn cooling through the scale comparisons between convection (Thorpe scale: LT) and wind (Monin-Obukhov: LMO): convective, wind-dominated mixing, and diurnal stratification. A diurnal-scale thermocline often developed under net heat input, suppressing vertical mixing, but was disrupted when either convective or wind intensified. Strong convection occurred under sustained surface heat loss (LT ≫ LMO), producing turbulence dissipation rates (εConvection ~ 10‒8–10‒7 W/kg), exceeding wind-driven values (εWind) by an order of magnitude. Conversely, wind forcing dominated (LMO ≫ LT), yielding εWind~10⁻7 W/kg. The mixed layer deepening correlated with w*3, but not with shear velocity. These findings refine mechanistic understanding of reservoir hydrodynamics and support ecologically sustainable management.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".