Blockage dynamics in sewer pipes: investigating snagging, accumulation, and dissipation rates of wet wipes
Bibliographic record
Abstract
ABSTRACT The increase in sewer blockages, exacerbated by the COVID-19 pandemic due to improper disposal of non-biodegradable items like wet wipes and sanitary products, along with fats, oils, grease, and the presence of defects in sewers, underscores the need for a more profound understanding of wipes’ contribution to these blockages. This study investigated the probability of wipe snagging on sewer imperfections and their subsequent accumulation and dissipation rates under various conditions through laboratory experiments. Findings indicated a significant variability in the likelihood of wipes snagging (ranging from 93% to 0) and accumulating (ranging from 71% to 0), which was influenced by the defect location and the sewer flow rate. Moreover, the study highlighted a direct relationship between the flow rate in the sewer and the rate at which wipes dissipate. Conversely, an inverse relationship was observed between the size of blockages and the dissipation rate of wipes, with larger blockages typically reducing the speed of this process. Importantly, while dissipation rates at low flow rates are nearly constant regardless of the number of wipes and duration, at medium to high flow rates, dissipation initially increases but levels off after 24 h, demonstrating the persistence of wipe-caused blockages in small-diameter sewer pipes.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".