Decomposition of 2D polygons and its effect in hydrological models
Bibliographic record
Abstract
Abstract 2D non-uniform polygonal meshes allow representation of the impact of landscape elements and small infrastructures on water flows. The initial vectorial mesh, derived from the intersection of several geographical information systems' layers, can have highly non-convex or sliver polygons. These bad-shaped elements compromise accurate numerical flow computation. We propose a flexible divide-and-conquer strategy to decompose polygons into physiographical meaningful parts using shape descriptors to better represent the surface terrain and hydrologic connectivity. We use the convexity index (CI) and the form factor (FF) to consider convex and square like optimum shapes. The strategy was applied to two peri-urban areas whose hydrologic response was simulated using distributed modeling. Good-quality meshes were generated with threshold values of CI≈0.8 and FF≈0.2, and CI≈0.95 and FF≈0.4 for undeveloped and highly urbanized zones, respectively. We concluded that the mesh segmentation facilitates the representation of the spatially distributed processes controlling not only the lumped response of the catchment, but also the spatial variability of water quantity and fluxes within it at medium and small scales.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".