Improving Bayesian maximum entropy and ordinary Kriging methods for estimating precipitations in a large watershed: a new cluster-based approach
Bibliographic record
Abstract
The main purpose of this research is to investigate spatial variations of mean annual precipitation in a watershed. As a case study, the research focused on the Namak Lake watershed in Iran. Literature provides various techniques for studying spatial patterns of precipitation in a watershed. These techniques often require a large dataset. On the other hand, nonuniform data distribution in a watershed can reduce the accuracy and reliability of the predictions. To overcome these problems, this research applied the cluster method coupled with ordinary Kriging and Bayesian maximum entropy techniques. An estimated point was modified based on the distance from the point to the cluster center. The research considered elevation variations as a secondary variable. A cross-validation technique was used for evaluating the results of mean annual precipitations. The research compared the results of ordinary Kriging and Bayesian maximum entropy methods with and without the application of the clustering method. The research concluded that the cluster-based method can estimate the dynamics of long-term mean annual precipitation more reliably and accurately. The research also revealed more informative results for the cluster-based method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".