Application of a Three‐Dimensional Coupled Hydrodynamic‐Ice Model for a Large and Deep Dimictic Lake Over Tibetan Plateau: Thermo‐Hydrodynamic Variations During 2007–2017
Bibliographic record
Abstract
Abstract The space‐time variations of thermo‐hydrodynamics and underlying mechanisms in Lake Nam Co, the third largest lake over Tibetan Plateau, are investigated using the simulations from a three‐dimensional lake‐ice coupled model during 2007–2017. The model well reproduces the seasonal lake thermodynamics, highlighting the phases of summer‐autumn warm thermal stratification, late‐autumn overturning, winter‐spring inverse thermal stratification, and late‐spring overturning. Heat budget analysis underscores the importance of lateral heat transport and ice freeze‐thaw processes in shaping the horizontal thermal variability. During 2007–2017, lake surface temperature, as well as the duration, onset and end of warm thermal stratification, show significant interannual variations related to the surface air temperature and ice conditions. During winter‐spring, the lake water flow speed shows strong interannual variability related to wind speed and ice conditions. Nevertheless, a consistent circulation pattern is found, featuring a dominant mid‐lake cyclonic gyre, upwelling along the western coast, and strong coastal currents driven by the prevailing southwesterly winds during December–January, followed by weakened lake water motions during February–April when the packed ice inhibits the wind stress input. In contrast, the summer‐autumn lake circulation is weaker but more variable, with the mid‐lake circulation shifting between being cyclonic (caused by the combined effects of southwesterly winds, positive wind stress curl and density effects) and occasionally anti‐cyclonic (due to the presence of negative wind stress curl).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".