Comparative Analysis of Short Time Increment Urban Precipitation Characteristics
Bibliographic record
Abstract
Some of the preliminary results obtained in a comparative analysis of short time increment rainfall characteristics observed in stations located in different climatic zones are presented in this paper. Such an analysis gives better insight to the characterization of short time increment rainfall processes. The probability distributions fitted to the storm (wet) durations and durations of dry periods are considered. The probability distributions fitted to the data from Boston, Mass., Tucson, Ariz., St. Johnsbury, Vt., Truro, Nova Scotia, Ely, Nev., and West Lafayette, Ind. are analyzed. The transition rate functions, the transition probabilities, the probabilities of storm age and of storm end states are compared. Secondly, the depth-duration relationships of the precipitation characteristics observed in some of the stations mentioned above are compared. It is found that some of the characteristics, such as the probability distributions of durations of wet periods in Boston, W. Lafayette, and St. Johnsbury are similar to each other although these stations are located in different climatic zones. In the same vein, the probability distributions of durations of dry periods are found to be close to each other for the data from Tucson, Ely and West Lafayette and Boston. The depth-duration relationships are found to be considerably different for many of these stations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".