Integrated environmental and clinical surveillance for the prevention of acute respiratory infections in closed settings and vulnerable communities: school, prison and nursing home (Stell-ARI Project)
Bibliographic record
Abstract
WBE is by now well known as a valuable tool to monitor the viral circulation in a community, to track emerging \nviruses/variants, to give early warning for the onset outbreaks. \nBut, to be really representative, WBE needs good quality data, coming from validated and standardized \ntechniques, identification of biases, correction and normalization of data. Many studies have analyzed the \ncorrelations between WBE for SARS-CoV2 and clinical data for COVID, finding variable results, depending on \nuncertainties, coming from both kinds of surveillance (1). \nThe lesson learned from COVID during the pandemic emergency can today be transferred to the surveillance \nfor the COVID itself, considering its present endemicity, as well as for other already endemic pathogens. \nNevertheless, to this aim several conditions must be satisfied, e.g. the pathogen elimination through feces, the \navailability of reliable detection methods, the persistence along the sewerage network, the relations with cases. \nIn order to explore the possibility of applying WBE to other pathogens we analyzed sewages collected for the \nSARI Italian Surveillance (2), also for Human Adenovirus (HAdV), Norovirus Genogroup II (NoVGGII), Non Polio \nEnterovirus (NPEV), Influenza virus (IV) and Respiratory Syncitial Virus (RSV), besides SARS-CoV2. Samples were \ntaken from 4 different Wastewater Treatment Plants (WWTPs), in the North of Tuscany (Italy), during a 12 \nmonths period and analyzed with the same methods applied for the SARI project.
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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 | 0.003 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".