Implications of climatic variations in temporal precipitation patterns for the development of design storms in Newfoundland and Labrador
Bibliographic record
Abstract
The distribution of precipitation in time is an important aspect for the development of design storms for storm water infrastructure design. The current set of mass curves used throughout the province of Newfoundland and Labrador (NL) may not be justified. To identify variation in mass curves across NL, and compare results with existing mass curves, hourly precipitation data from 10 stations were used. Bayesian k-means clustering was used to identify dimensionless mass curves to represent precipitation patterns. Eight distinct temporal patterns of precipitation were identified and further regrouped into four, useful for making recommendations on the choice of mass curve. Crosstabulation applied to the patterns were found to be significantly influenced by event duration, depth, and climate zone. Results support the conclusion that climate was an important determinant of temporal distribution of precipitation, and it is important to determine which pattern is dominant in a given region.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".