Wastewater Surveillance of SARS-CoV-2 at a Canadian University Campus and the Impact of Wastewater Characteristics on Viral RNA Detection.
Bibliographic record
Abstract
Because of the increased population\ndensity, high-risk behavior\nof young students, and lower vaccination rates, university campuses\nare considered hot spots for COVID-19 transmission. This study monitored\nthe SARS-CoV-2 RNA levels in the wastewater of a Canadian university\ncampus for a year to provide actionable information to safely manage\nCOVID-19 on campus. Wastewater samples were collected from the campus\nsewer and residence buildings to identify changes, peaks, and hotspots\nand search for associations with campus events, social gatherings,\nlong weekends, and holidays. Furthermore, the impact of wastewater\nparameters (total solids, volatile solids, temperature, pH, turbidity,\nand UV absorbance) on SARS-CoV-2 detection was investigated, and the\nefficiency of ultrafiltration and centrifugation concentration methods\nwere compared. RT-qPCR was used for detecting SARS-CoV-2 RNA. Wastewater\nsignals largely correlated positively with the clinically confirmed\nCOVID-19 cases on campus. Long weekends and holidays were often followed\nby increased viral signals, and the implementation of lockdowns quickly\ndecreased the case numbers. In spite of online teaching and restricted\naccess to campus, the university represented a microcosm of the city\nand mirrored the same trends. Results indicated that the centrifugation\nconcentration method was more sensitive for wastewater with high solids\ncontent and that the ultrafiltration concentration method was more\nsensitive for wastewater with low solids content. Wastewater characteristics\ncollected from the buildings and the campus sewer were different.\nStatistical analysis was performed to manifest the observations. Overall,\nwastewater surveillance provided actionable information and was also\nable to bring high-risk factors and events to the attention of decision-makers,\nenabling timely corrective measures.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".