Antecedent soil moisture conditions of different soil types in South‐western Ontario, Canada
Bibliographic record
Abstract
Abstract Soil moisture conditions prior to input design storms need to be known in the planning and design of urban stormwater control facilities using the design storm approach. Limited information is available on these soil moisture conditions which are commonly referred to as the antecedent soil moisture conditions. In this study, a deterministic continuous simulation model was used to simulate antecedent soil moisture conditions under south‐western Ontario, Canada, climate conditions. A wide range of different soil types were investigated and various statistical analyses on the simulated antecedent soil moisture results were performed. Frequency analyses illustrated typical distributions of antecedent soil moisture conditions and the influence of finer and coarser textured soil particles. Empirical equations were developed for the estimation of average antecedent soil moisture conditions based on commonly known soil characteristics. Satisfactory performance of the empirical equations was demonstrated by comparing between field average antecedent soil moisture data and empirically estimated average antecedent soil moisture values. These equations are therefore recommended for use in urban stormwater studies incorporating the design storm approach. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".