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Record W3185267796 · doi:10.1080/09603123.2021.1949437

Global occurrence of SARS-CoV-2 in environmental aquatic matrices and its implications for sanitation and vulnerabilities in Brazil and developing countries

2021· review· en· W3185267796 on OpenAlexaff
Gleyson B. Castro, Aline Christine Bernegossi, Bruno José de Oliveira Sousa, Mara R. de Lima e Silva, Fernando Rodrigues-Silva, Bárbara Luíza Souza Freitas, Allan Pretti Ogura, Juliano José Corbi

Bibliographic record

VenueInternational Journal of Environmental Health Research · 2021
Typereview
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsQueen's University
Fundersnot available
KeywordsSanitationDeveloping countryBusinessEnvironmental planningEnvironmental healthStormwaterWater supplyEnvironmental scienceEnvironmental protectionEnvironmental engineeringEconomic growthMedicineEcologyEconomicsSurface runoffBiology

Abstract

fetched live from OpenAlex

This paper includes a systematic review of the SARS-CoV-2 occurrence in environmental aquatic matrices and a critical sanitation analysis. We discussed the interconnection of sanitation services (wastewater, water supply, solid waste, and stormwater drainage) functioning as an important network for controlling the spread of SARS-CoV-2 in waters. We collected 98 studies containing data of the SARS-CoV-2 occurrence in aquatic matrices around the world, of which 40% were from developing countries. Alongside a significant number of people infected by the virus, developing countries face socioeconomic deficiencies and insufficient public investment in infrastructure. Therefore, our study focused on highlighting solutions to provide sanitation in developing countries, considering the virus control in waters by disinfection techniques and sanitary measures, including alternatives for the vulnerable communities. The need for multilateral efforts to improve the universal coverage of sanitation services demands urgent attention in a pandemic scenario.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.949
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.258
GPT teacher head0.528
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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