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Record W4323364968 · doi:10.1016/j.coesh.2023.100458

Moving forward with COVID-19: Future research prospects of wastewater-based epidemiology methodologies and applications

2023· review· en· W4323364968 on OpenAlexaff
Guangming Jiang, Yanchen Liu, Song Tang, Masaaki Kitajima, Eiji Haramoto, Sudipti Arora, Phil M. Choi, Greg Jackson, Patrick M. D’Aoust, Robert Delatolla, Shuxin Zhang, Ying Guo, Jiangping Wu, Yan Chen, Elipsha Sharma, Tanjila Alam Prosun, Jiawei Zhao, Manish Kumar, Ryo Honda, Warish Ahmed, Jonathan Meiman

Bibliographic record

VenueCurrent Opinion in Environmental Science & Health · 2023
Typereview
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsUniversity of Ottawa
FundersDepartment of Industry, Science, Energy and Resources, Australian GovernmentTsinghua UniversityNational Natural Science Foundation of ChinaAustralian Research CouncilAustralian Academy of Science
KeywordsWastewaterCoronavirus disease 2019 (COVID-19)Transmission (telecommunications)Environmental scienceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Sampling (signal processing)2019-20 coronavirus outbreakPandemicStability (learning theory)Risk analysis (engineering)Biochemical engineeringComputer scienceBiologyEnvironmental engineeringEngineeringVirologyDiseaseBusinessMedicineTelecommunicationsMachine learningInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0090.004

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 head0.523
GPT teacher head0.570
Teacher spread0.047 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations38
Published2023
Admission routes1
Has abstractno

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