Wetland Modeling in PCSWMM: Exploring Options to Define Wetland Features and Incorporate Groundwater Exchanges
Bibliographic record
Abstract
Wetlands in Southern Ontario are experiencing degradation from urban development.Hydrologic analysis is important to demonstrating that a development will not have negative impacts on wetlands.A wetland water balance study has been conducted to develop reference hydrologic regimes for two wetlands in Pickering, Ontario.Knowledge gained from wetland water balance analysis informed the development of PCSWMM models.The objective of this study was to explore options for incorporating and defining wetlands in PCSWMM, select groundwater interaction parameters, and optimize the process for creating a calibrated catchment-wetland model using known seasonal wetland water levels.Continuous monitoring data was used for calibration and validation of the models.Methods, calibration targets, and challenges encountered when defining groundwater interactions and stage-storage relationships will be highlighted.The study will identify areas where increased data collection could improve the model parameterization.The discussion will address the value of a model which can both adequately represent the development and the wetland by incorporating infiltration-runoff effects, evapotranspiration changes, LID practices, groundwater regimes, and dynamic feedbacks between the wetland water level and the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".