A data set of global river networks and corresponding water resources zones divisions v2
Bibliographic record
Abstract
The scale and topological relationship of river networks (RN) and water resources zones (WRZ) directly affect the simulation results of global multi-scale hydrological cycle and the accuracy of water resource refined evaluation. However, few existing global hydrological data sets take account of both aspects simultaneously. Here, we constructed a new hydrologic data set with a spatial resolution of 90 m as an upgraded version of the GRNWRZ V1.0. This data set had proper grading and partitioning thresholds and clear coding of topological relationships. Based on maintaining the accuracy of river networks in the GRNWRZ V1.0, we determined the more refined thresholds and created a new coding rule, which made the grading RN and partitioning WRZ more precise and the topological relationship more intuitive. Supported by this data set, the accuracy and efficiency of the large-scale hydrological simulation can be guaranteed. This data set provides fundamental data support for global water resources governance and global hydrological modeling under climate change.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.032 |
| Research integrity | 0.000 | 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 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".