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Record W1979907895 · doi:10.2166/ws.2012.026

Assessment of alternate characterization tests for prediction of taste and odour control by granular activated carbon

2012· article· en· W1979907895 on OpenAlexafffund
K.M. Smith, Xiaodan Zhang, Ron Hofmann

Bibliographic record

VenueWater Science & Technology Water Supply · 2012
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsWSP (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsActivated carbonAdsorptionChemistryIodineCarbon fibersCharacterization (materials science)ChromatographyChemical engineeringEnvironmental chemistryNanotechnologyOrganic chemistryMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The acetoxime number test was recently identified by the American Society for Testing and Materials as a method to characterize activated carbon. This adsorption isotherm gives information about the number of high energy adsorption sites on the surface of an activated carbon. A similar test uses tetrafluoromethane for the same purpose. In contrast, the more common iodine number and Brunauer, Emmet and Teller (BET) surface area parameters are a measure of the total number of adsorption sites. 2-Methylisoborneol (MIB) is reported to require high energy sites for removal, so it was hypothesized that data from acetoxime and tetrafluoromethane isotherm tests for different activated carbons would be better correlated to MIB removal than iodine numbers or BET surface areas. MIB breakthrough was monitored in rapid small-scale column tests (RSSCTs) using four natural waters and five activated carbons. Results showed no correlation between acetoxime or tetrafluoromethane isotherms and MIB breakthrough.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.438

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 head0.010
GPT teacher head0.238
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 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

Citations7
Published2012
Admission routes2
Has abstractyes

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