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Record W2069992946 · doi:10.15376/biores.7.4.5794-5808

PHYSICOCHEMICAL PROPERTIES ANALYSIS AND SIZE DISTRIBUTION RESEARCH OF MICROSTICKIES IN WHITEWATER

2012· article· en· W2069992946 on OpenAlexaff
Zhiwei Wang, Bo Li, Shubin Wu, Peng Lü

Bibliographic record

VenueBioResources · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of New Brunswick
FundersFundamental Research Funds for the Central UniversitiesState Key Laboratory of Pulp and Paper EngineeringNational Natural Science Foundation of ChinaUniversity of South ChinaSouth China University of TechnologyCentral University Basic Research Fund of China
KeywordsWaste paperEconomies of agglomerationDeposition (geology)Process engineeringComputer scienceEnvironmental engineeringEnvironmental scienceChemical engineeringWaste managementEngineeringGeology

Abstract

fetched live from OpenAlex

Microstickies in whitewater have caused serious deterioration of paper quality and low efficiency of paper machine runnability. To solve this problem it is necessary to master the characteristics of various aspects of microstickies. In this study, the physicochemical properties and size distribution of microstickies in whitewater of three typical kinds of waste papers, old newspaper (ONP), old book paper (OBP), and mixed office wastepaper (MOW), were investigated by conventional methods and a modified Flow Cytometry Method (FCM). The results showed that white water microstickies in different kinds of waste paper have different characteristics. This is a premise for analyzing stickies problems. Furthermore, in a certain kind of waste paper, the physicochemical properties and the direct determination of size and number of microstickies particle in whitewater can be combined together and taken as a whole to account for more phenomena or deduce more mechanisms, such as agglomeration and deposition, etc.

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 score0.327
Threshold uncertainty score0.169

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)0.0000.000

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 head0.023
GPT teacher head0.250
Teacher spread0.227 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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