MétaCan
Menu
Back to cohort
Record W1968706971 · doi:10.1246/cl.140102

Comparison between Ion-chromatography and Titration Methods for the Determination of Sulfite in Wastewater Containing Furfural

2014· article· en· W1968706971 on OpenAlexaff
Yufeng Wang, Kefu Chen, Qiang Wang, Zhibin He, Lei Zheng, Yonghao Ni

Bibliographic record

VenueChemistry Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsChemistrySulfiteTitrationFurfuralChromatographyIon chromatographyWastewaterIonOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract Sulfur-containing species, such as sulfite, are detrimental to the operation of anaerobic systems, therefore, it is critical to determine their concentrations in order to optimize an anaerobic reactor. In this study, the traditional titration method for the determination of sulfite was applied to acid-condensate samples from an acid sulfite pulp mill effluent, and the results showed that they were consistently lower than those from ion chromatography (IC). It was found that the presence of furfural in the acid-condensate samples can interfere with the titration for sulfite, resulting in lower sulfite values; the higher the furfural concentration in the sample, the lower the indicated sulfite concentration by titration. It was concluded that IC method is a reliable method for determining the sulfite concentration for samples containing furfural.

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)0.004
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.302
Teacher spread0.279 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueChemistry LettersSame topicIndustrial Gas Emission ControlFrench-language works237,207