Identifying Source Hotspots of “Non-Flushables” in Sewer Systems Through Machine Learning and Imaging Sensors
Bibliographic record
Abstract
This thesis examines the feasibility of installing imaging sensors in sewers, combined with innovative machine learning techniques, to detect and identify non-flushable consumer products in sewers. A Raspberry Pi microprocessor with an off-the-shelf camera module was used, and Edge Impulse was applied to process captured imagery. The results indicated that optimal placement of the system (camera and lights) can vary depending on whether the products of interest float near the surface of the water or more towards the deep end of the sewer maintenance holes. The application of such a system for urban wastewater collection systems will be to proactively detect the areal hotspots of rising influxes of non-flushable consumer products (e.g., wet wipes and tissues). Building on knowledge gained on the performance and functionality of such a monitoring tool, utility managers can perform targeted public outreach, as opposed to general calls to the public to stop flushing consumer products.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".