Developing a Regional Correlation Function for Rainfall near Hamilton, Ontario
Bibliographic record
Abstract
In this paper, we develop a model for the second order process of rainfall in an area of southern Ontario. Hourly rainfall data collected from 17 gauges over a period of five years were used to develop a correlation function for rainfall. Eighteen test cases were used to investigate the optimized range, the appropriate function form, and the impacts of rainfall type and gauge error on the estimated correlation function. An exponential model for the correlation function was selected over a spherical and gaussian model. It is suspected that gauge error greatly affects the size of the range, which decreases as the uncertainty in the data increases. Three distinct values of range were determined and the differences in range were attributed to the number of thunderstorms observed in the region during the test periods. The range spanned from 26 km in periods of heavy thunderstorm activity to 75 km in periods of mostly cyclonic activity. High values of the coefficient of determination were found when combining years of data with range values of just over 50 km. The difference in the standard error associated with a rain gauge network can be significant depending on the range chosen. With regard to devising rain gauge networks to determine average rainfall, an average range of 50 km is recommended.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".