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Record W2167113893 · doi:10.2166/wqrj.2001.036

Fractal Analysis of Pore Distributions in Alum Coagulation and Activated Sludge Flocs

2001· article· en· W2167113893 on OpenAlexaff
Beata Gorczyca, Jerzy J. Ganczarczyk

Bibliographic record

VenueWater Quality Research Journal · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicCoagulation and Flocculation Studies
Canadian institutionsUniversity of TorontoUniversity of Manitoba
Fundersnot available
KeywordsPorositySettlingAlumCoagulationPermeability (electromagnetism)Aggregate (composite)FractalActivated sludgeFractal dimensionChemistryMineralogyChemical engineeringMaterials scienceChromatographyComposite materialEnvironmental engineeringWastewaterEnvironmental scienceMetallurgyMathematicsMembrane

Abstract

fetched live from OpenAlex

Abstract The information on floc porosity is essential for estimation of the permeability of the aggregate. The average porosity of alum and activated sludge flocs determined in this study on thin sections of the aggregates was similar, varying in the range from 8 to 9%. Similar porosity of the two different types of aggregates suggested that the permeability of these flocs could also be similar. However, the experimental observations of floc settling rates and floc shape factors did not support this expectation and led to a conclusion that the permeability of an aggregate cannot be estimated based on the average geometric porosity of a floc only. When the size distributions of pores on flocs' sections were analyzed using the concept of fractal geometry, different characteristic values for alum coagulation and activated sludge flocs were found. Larger pore size found in activated sludge flocs allows for more flow through these flocs. Therefore, the size of characteristic pores rather than the average total porosity determines the permeability of an aggregate.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
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 head0.134
GPT teacher head0.424
Teacher spread0.290 · 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 designBench or experimental
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

Citations27
Published2001
Admission routes1
Has abstractyes

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