Spatial patterns of homogeneous pooling groups for flood frequency analysis
Bibliographic record
Abstract
Abstract Several methods for the exploration and modelling of spatial point patterns are introduced to study the spatial patterns of homogeneous pooling groups for flood frequency analysis. The study is based on selected catchments in Great Britain, where a high density of gauging stations has been established. Initial pooling groups are formed using the K-means clustering algorithm with appropriately selected similarity measures. The pooling groups are subsequently revised to improve the homogeneity in the hydrological response. Spatial patterns of the initial and final pooling groups are explored in terms of intensity and dependence of the spatial distribution of the catchments. A test against a spatial point process is used to confirm or reject the initial impression of spatial clustering. Changes in the spatial patterns from the initial to the final pooling groups are examined using two comparison methods. The spatial pattern analysis described above can be used to answer the following questions: whether homogeneous catchments tend to exist in the vicinity of each other; whether the improvement in homogeneity tends to form more clustered pooling groups; and how the spatial patterns observed can be used to direct the selection of pooling variables.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".