Modelling the Thermal Structure and Circulations of Lake Nam Co, Central Tibetan Plateau
Bibliographic record
Abstract
A three-dimensional (3-D) hydrodynamic model based on Princeton Ocean Model (POM) and a one-dimensional (1-D) lake model are applied to simulate the thermal structure and circulations of Lake Nam Co (LNC), the second largest lake in Tibet. Results show that POM can well reproduce the seasonal and synoptic variations of the in-situ observed vertical temperature profile, and the spatial distribution of satellite estimated lake surface temperature during May-December 2013. However, without considering the water and energy exchanges related to the lake hydrodynamics, the 1-D model exhibits much more evident biases in the lake thermal evolution. These shortages of the 1-D lake model solutions emphasize that the complex temperature-current interactions must be accounted for investigating the thermodynamics in large lakes over Tibet. From both observation and hydrodynamic simulations, LNC is identified to experience the springtime overturning, warm stratified phase during early-June to mid-November, autumnal overturning, and weak inverse stratified phase since mid-December. The two overturning processes last for about one month and are both related to the thermal bar development, which is controlled by the density-driven convection associated with the radiative heating (surface cooling) in spring (autumn). During the warm stratified phase, the eastern shallow basin is mainly characterized by anticyclonic circulation and bowl-shaped thermocline, while the central deep basin is featured by a cyclonic gyre (eastward currents) and dome-shaped (bowl-shaped) thermocline with the enhancement (weakness) of thermal stratification. The lake circulation during December is basically dominated by a single strong cyclonic gyre in the main lake basin.
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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 | 0.001 | 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".