Bibliographic record
Abstract
In the 20 y period since the advent of the area-velocity flow monitor, the development of hydraulic models has greatly improved the planning and design of two of the three solutions to overflows and basement backups which sometimes result when excessive stormwater enters the sanitary sewer system.The three general solutions for overflows and basement backups are: prevention of stormwater from entering the system (inflow-infiltration, I/I, reduction), storage facilities for excess stormwater, and increased conveyance capacity to the downstream treatment plant.Of these three, only the design and planning of I/I reduction has not substantially benefited from hydraulic modeling improvements.This is unfortunate as it is the greenest, and often the least costly, of the solutions.Micromonitoring is a new tool which greatly improves the design and planning of such I/I removal as a solution.Computing power on the desk of an engineer today was only available through universities and government research institutions twenty years ago.The advent of geographic information systems (GIS) provided hydraulic modelers with a wealth of ready and useful digital data.Combined, these advances transformed hydraulic models from merely trunk sewer models to all pipe models with base flows generated from water use records at every house.However, the ability to compute the generation of base flow in every pipe did nothing to supply the detailed information about the storm flow generated by every pipe.The more detailed models improved the planning and design of storage and conveyance solutions downstream of the flow meters, but remained little more than estimates (or guesses) of the distribution of those flows upstream of the flow monitors.The real benefit of the increased detail in the models upstream of the flow monitors was accurate stage-storage and stage-discharge curves upstream in those areas during sewer surcharge.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".