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Record W2724683122

Investigating the Fundamental Parameters of Cake Filtration using a Gravity Column Device

2014· dissertation· en· W2724683122 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTSpace (University of Toronto) · 2014
Typedissertation
Languageen
FieldEnvironmental Science
TopicCoagulation and Flocculation Studies
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsFiltration (mathematics)Column (typography)ChromatographyMaterials scienceEnvironmental scienceProcess engineeringChemistryMathematicsEngineeringMechanical engineeringStatistics
DOInot available

Abstract

fetched live from OpenAlex

A column device equipped with an imaging system was used to estimate the permeability of filter mesh, and the porosity and permeability of filter cakes formed by the filtration of wastewater. Synthetic wastewater samples containing polyethylene microspheres with mono-sized and bi-modal size distributions prepared and the effect of particle size and its distribution on filter cake permeability and porosity were investigated. Using actual wastewater samples, changes in filter cake porosity and permeability during the gravity drainage process were investigated. Based on the initial slope of the drainage curve, the filter mesh permeability was estimated. A mathematical model was developed based on Darcy's law to predict the drainage rate and the height of wastewater during the column filtration process with an average error of less than 7%. Experimental drainage data collected for various water column heights suggest that cake compressibility may play a role in the drainage of wastewater.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.997

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)0.0040.000

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 head0.034
GPT teacher head0.268
Teacher spread0.234 · 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