Water Isotope framework for lake water balance monitoring and modelling in the Nam Co Basin, Tibetan Plateau
Bibliographic record
Abstract
Study region The Nam Co, the second largest and highest saline lake in central Tibetan Plateau, China. Study focus Since the establishment of the Nam Co research station, a large number of water isotope measurements in the watershed was accumulated. There is a strong need to establish an isotope framework to benefit long-term monitoring and model development. Further discussions of the water balance of the lake and water yield of the basin are also possible under an isotope framework. New hydrological insights A water isotope framework for the Nam Co basin, including the Local Meteoric Water Line, limiting isotopic composition of evaporation and two hypothetical evaporation trajectories, is established. We further applied the isotope mass balance model to estimate the overall isotopic composition of input water to the Nam Co, the evaporation over inputs ratios (E/I) for three consecutive years, and the water yields (Wy, depth equivalent runoff) at a basin scale. Our results clearly suggest a positive water budget (i.e., E/I < 1), providing another line of evidence that the subsurface leakage from Nam Co is likely. The discrepancy between isotope-based water yields estimations and field-based runoff observations suggest that, compared to the well-studied Nyainqentanglha Mountains and southwestern mountains, the ridge-and-valley landscape in the western highlands and northwestern hogbacks are possibly low yields area, which should draw more research attentions in future hydrological investigations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".