An Efficient and Structured Procedure to Develop Conceptual Catchment and Sewer Models from Their Detailed Counterparts
Bibliographic record
Abstract
Modelling flow rates in catchments and sewers with a conceptual, also known as hydrological, approach is widely applied if fast simulations are important. In cases where a detailed hydrodynamic model exists, it is common to start conceptualizing from this detailed counterpart. Unfortunately, no generalized procedure exists, which is surprising as this can be a complex and time-consuming task. This research work proposes a procedure that is validated with two independent combined sewer case studies. The conceptual models provide the targeted results with respect to representation of the flow rates and reduction in the computational time. As the desired performance could be reached for different levels of model aggregation, it is concluded that the conceptual model can be tailored to the points where accurate flow rates need to be predicted. Furthermore, the comparison of the conceptual model results with flow measurements highlights the importance of analyzing and eventually compensating for the limitations of the detailed model.
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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".