Probabilistic Approaches for Assessment of Non-Point Source Pollutant Loads from Urban Watersheds
Bibliographic record
Abstract
Estimation of non-point source pollution has been a major challenge for many urbanized and urbanizing watersheds. The estimation of loads from non-point sources particularly from urban watersheds is very complex due to many factors that include the random variations of rainfall, runoff, pollutant buildup and washoff and the overall complexity of the watershed. Thus, it is appropriate to analyze the urban stormwater pollution within a probabilistic setting. As an alternative approach to continuous simulation, analytical probabilistic models have been proposed for analyzing urban runoff quality control systems. Based on concepts from statistics and probability theory, these models have been derived from the probabilistic characteristics of system input using the functional relationship between inputs and outputs. Exponential probability density functions are used to express the probabilistic behavior of long-term rainfall characteristics. Buildup functions and washoff functions, analogous to those used in continuous simulation models, are used. The models are verified with available field data from an urban watershed located in the Greater Toronto Area and reasonable agreements are obtained between model results and observed data.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".