Development of Long Term Precipitation and Infiltration Records for the Performance Evaluation of a Proposed Regional Tunnel
Bibliographic record
Abstract
This chapter describes how long-term precipitation and infiltration records were developed for use in the performance evaluation of a proposed regional drainage tunnel shared by the cities of Detroit and Dearborn, Michigan.The precipitation record includes homly precipitation data (rainfall plus snowfall) plus a calculation tor snowmelt.Methodologies were applied to this record to develop 36 y of rainfall and snowfall plus snowmelt at 15-min intervals.Infiltration parameters are varied monthly based on long-term rainfall/nmoff records, rather than assumed to be constant throughout the year.In addition, allO\vances have been made for incorporation of spatially non-uniform precipitation into the evaluation.The application of these precipitation and infiltration records has allowed an improved representation of the hydrologic factors contributing to combined sewer overflow (CSO) in Southeast Michigan over an extended simulation length.The implementation of these records has improved the capabilities of the continuous model for computing the long-•term performance of proposed CSO facilities in Southeast Michigan, including the Proposed Regional Tunnel Project.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".