Unveiling the Targeted Opportunities and Universal Challenges of Wastewater-Based Surveillance for Public Health
Bibliographic record
Abstract
ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTUnveiling the Targeted Opportunities and Universal Challenges of Wastewater-Based Surveillance for Public HealthCresten Mansfeldt*Cresten MansfeldtDepartment of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, UCB 428, Boulder, Colorado 80309, United StatesEnvironmental Engineering Program, University of Colorado Boulder, UCB 607, Boulder, Colorado 80309, United States*[email protected]More by Cresten MansfeldtView Biographyhttps://orcid.org/0000-0002-5656-6463, Rasha Maal-BaredRasha Maal-BaredQuality Assurance and Environment, EPCOR Water Services Inc., EPCOR Tower, 2000−10423 101 Street NW, Edmonton, Alberta T5H 2R8, CanadaMore by Rasha Maal-Bared, Devrim KayaDevrim KayaSchool of Chemical, Biological, and Environmental Engineering, Oregon State University, 105 26th Street, Corvallis, Oregon 97331, United StatesMore by Devrim Kaya, Devin A. BowesDevin A. BowesCenter on Forced Displacement, Boston University, 111 Cummington Mall, Boston, Massachusetts 02215, United StatesMore by Devin A. Bowes, Ishi KeenumIshi KeenumComplex Microbial Systems Group, Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United StatesMore by Ishi Keenum, Srijan AggarwalSrijan AggarwalDepartment of Civil, Geological, and Environmental Engineering, University of Alaska Fairbanks, 1764 Tanana Loop, Fairbanks, Alaska 99775, United StatesMore by Srijan Aggarwalhttps://orcid.org/0000-0002-9141-9936, Ananda TiwariAnanda TiwariDepartment of Food Hygiene and Environmental Health, Faculty of Veterinary Medicine, University of Helsinki, Agnes Sjöberginkatu 2, P.O. Box 66, FI 00014 Helsinki, FinlandMore by Ananda Tiwari, and Justin M. HutchisonJustin M. HutchisonCivil, Environmental, and Architectural Engineering, University of Kansas, 1530 West 15th Street, Lawrence, Kansas 66045, United StatesMore by Justin M. Hutchisonhttps://orcid.org/0000-0002-5575-6169Cite this: ACS EST Water 2023, 3, 8, 1987–1989Publication Date (Web):June 21, 2023Publication History Received5 June 2023Accepted9 June 2023Revised9 June 2023Published online21 June 2023Published inissue 11 August 2023https://pubs.acs.org/doi/10.1021/acsestwater.3c00291https://doi.org/10.1021/acsestwater.3c00291article-commentaryACS PublicationsCopyright © 2023 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views883Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (2 MB) Get e-AlertscloseSUBJECTS:Computer simulations,SARS-CoV-2,Solid waste,Wastes,Wastewater Get e-Alerts
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 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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".