Precipitation bias variability<i>versus</i>various gauges under different climatic conditions over the Third Pole Environment (TPE) region
Bibliographic record
Abstract
An international programme dedicated to the study of the Third Pole Environment (TPE) is now developing. The TPE region is centred on the Tibetan Plateau and concerns the interests of the surrounding countries and regions. To improve input for hydrological research, we collected precipitation data on 241 meteorological stations across the TPE region; these data were obtained from various countries, thus including various types of gauges. Employing the procedure recommended by the World Meteorological Organization (WMO), a full version of bias adjustment was applied to the data, including adjustments for wind-induced error, wetting loss, evaporation loss and trace amount for each station. The results reveal that the average annual precipitation has increased considerably from a minimum of 4 mm to a maximum of 409 mm with an overall mean of 27% from the adjustment, the largest bias being found in the Chinese standard precipitation gauge (CSPG) which was used in the central TPE region. In addition, the bias shows variable spatial and temporal patterns in different climate zones throughout this area. It is expected that this study and its results will be beneficial for hydrological and climatic studies over the TPE region.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".