Numerical Study of the Thermal Structure and Circulation in a Large and Deep Dimictic Lake Over Tibetan Plateau
Bibliographic record
Abstract
Abstract A three‐dimensional (3‐D) hydrodynamic model based on the Princeton Ocean Model (POM) was applied to simulate the thermal structure and circulation of Lake Nam Co (LNC), the third largest lake over the Tibetan Plateau (TP), during May–December 2013. Compared with a spatially distributed set of one‐dimensional thermal diffusion lake models, POM better reproduced the observed seasonal evolution of the horizontal distribution of lake surface temperature and the vertical thermal structure. A heat budget analysis confirmed that the lateral heat exchange made significant contributions to the horizontal variability of lake temperature. The model results showed that LNC was thermally stratified in summer, had a weak inverse stratification since mid‐December, and was fully turned over during late spring and autumn. During both overturning phases, the modeled “thermal bar” was developed as a result of the density‐driven convection in response to the radiative heating (surface cooling) during spring (autumn). The 3‐D model results showed that the monthly mean circulation featured a predominant mid‐lake cyclonic gyre throughout the ice‐free period; upwelling along the western coast and strong coastal currents occurred in all months except in July–August. Model sensitivity experiments confirmed that the lake circulation was primarily driven by the barotropic dynamics of the prevailing southwesterly wind, while the baroclinic process made a secondary contribution. The results pointed out the necessity to resolve lateral processes when modeling large TP lakes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".