Assessment of the Regionalization of Precipitation in Two Canadian Climate Regions: A Fuzzy Clustering Approach
Why this work is in the frame
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Bibliographic record
Abstract
Regional frequency analysis (RFA) is used to obtain reliable estimates of local precipitation events for a variety of applications in water resources engineering. The focus of the presented research is on an initial step of the RFA process; that is the formation of precipitation regions (also referred to as regionalization). The aim of this study is to dissect the regionalization procedure into its individual components that require subjective user input, and to evaluate their respective influences on the results. All assessments are conducted in two of Canada's climate regions; namely the Prairie and Great Lakes-St. Lawrence lowlands. Additionally, a new fuzzy clustering approach to regionalization that uses optimization is proposed. It is evident that the outcomes are sensitive to the choice of the regionalization method, the number of regions into which the sites of the study area are partitioned, the climate site attributes and the temporal resolution of the precipitation data. Recommendations for the selection of such factors are provided based on their application.
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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 | 0.001 | 0.000 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 it