Role of wastewater treatment in COVID-19 control
Bibliographic record
Abstract
Abstract The International Water Association (IWA) initiated a Task Force in April 2020 to serve as a leadership team within IWA whose role is to keep abreast and communicate the emerging science, technology, and applications for understanding the impact and the ability to respond to the COVID-19 pandemic and specifically designed for water professionals and industries. Expertise was nominated across the world with the purpose of collectively providing the water sector with knowledge products for the guidance on the control of COVID-19 and other viruses. This review paper developed by a working group of the IWA Task Force focuses on the control of COVID-19. The purpose of this review paper is to provide an understanding of existing knowledge with regards to COVID-19 and provide the necessary guidance of risk mitigation based on currently available knowledge of viruses in wastewater. This review paper considered various scenarios for both the developed world and the developing world and provided recommendations for managing risk. The review paper serves to pool the knowledge with regards to the pandemic and in relation to other viruses. The IWA Task Team envisage that this review paper provides the necessary guidance to the global response to the ongoing pandemic.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".