Characteristics of the urban sewer system and rat presence in Seattle
Bibliographic record
Abstract
Abstract Rats are abundant and ubiquitous in urban environments. There has been increasing attention to the need for evidence-based, integrated rat management and surveillance approaches because rats can compromise public health and impose economic costs. Yet there are few studies that characterize rat distributions in sewers and there are no studies that incorporate the complexity of sewer networks that encompass multiple sewer lines, all comprised of their own unique characteristics. To address this knowledge gap, this study identifies sewer characteristics that are associated with rat presence in the city of Seattle’s urban sewer system. We obtained sewer baiting data from 1752 geotagged manholes to monitor rat presence and constructed generalized additive models to account for spatial autocorrelation. Sewer rats were unevenly distributed across sampled manholes with clusters of higher rat presence at upper elevations, within sanitary pipes, narrower pipes, pipes at a shallower depth, and older pipes. These findings are important because identifying features of urban sewers that promote rat presence may allow municipalities to target areas for rat control activities and sewer maintenance. These findings suggest the need to evaluate additional characteristics of the surface environment and identify the factors driving rat movement within sewers, across the surface, and between the surface and the sewers.
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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 | 0.001 |
| 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) | 0.001 | 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".