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Record W4311164384 · doi:10.15376/biores.18.1.hadley

Flushed but not forgotten: The rising costs and opportunities of disposable wet wipes

2022· article· en· W4311164384 on OpenAlexaff
Tilda Hadley, Kate Hickey, Kelsi Lix, Shivani Sharma, Tyler Berretta, Titichai Navessin

Bibliographic record

VenueBioResources · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsUniversity of British Columbia
FundersNorth Carolina State University
KeywordsBusinessConfusionCoronavirus disease 2019 (COVID-19)Waste managementEngineeringMedicine

Abstract

fetched live from OpenAlex

The increasing popularity of single-use wet wipes across a variety of applications has caused environmental and economic challenges. Due to their convenience and low cost, disposable nonwoven wipes have become a necessity in the lives of many. However, consumers rarely consider the end-of-life of these items. Despite efforts from stakeholders, including wipes manufacturers and wastewater experts, there is frequent confusion among consumers regarding appropriate disposal. Many consumers flush wipes that are not compatible with municipal sewer systems, causing considerable damage. Additionally, wipes have poor environmental outcomes, as they often contain non-renewable plastics or are unable to biodegrade under disposal conditions. Previously, the wet wipes industry was projected to grow an average of 6% between 2021 and 2025; however, the use of these disposable items is projected to be much higher due to the COVID-19 pandemic. This paper reviews the market, key challenges, and technical properties of single-use nonwoven wipes. An emphasis is placed on the unique properties and associated challenges of flushable wipes. With strong market demands, consumers are unlikely to abandon single-use wipes, and therefore innovative solutions are required to solve the main environmental and technical challenges associated with flushable and non-flushable wipes.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.204
Teacher spread0.183 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations11
Published2022
Admission routes1
Has abstractyes

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