Statistical analysis of sewer odour based on 10-year complaint data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The City of Edmonton has been suffering from sewer odour problem for many years. Ten years of odour complaints data from 2008 to 2017 were statistically analyzed to identify major factors that relate to the odour problem. Spatial and temporal distributions of odour complaints in the city were first presented. Then relationships between the complaints and physical attributes of the sewer systems were analyzed by introducing a parameter of risk index. It was found that the snowmelt and storm events could possibly reduce odour complaints. Old sewer pipes and large drop structures are statistically more linked and thus significantly contribute to the complaints. The risk index relationship for three pipe materials is clay pipe > concrete pipe > PVC pipe. Combined sewers are more problematic in terms of odour complaints than sanitary sewers. And no clear correlation has been found between the changes of sewer pipe slope or angle and the complaints.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it