MétaCan
Menu
Back to cohort
Record W1975160068 · doi:10.1080/09593330.2012.733415

The removal of anionic surfactants from water in coagulation process

2012· article· en· W1975160068 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.

Bibliographic record

VenueEnvironmental Technology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Chemistry and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsCoagulationPulmonary surfactantChemistryPowdered activated carbon treatmentWater treatmentCationic polymerizationSulfatePolyelectrolyteChemical engineeringPulp and paper industryInorganic chemistryChromatographyActivated carbonWaste managementAdsorptionOrganic chemistry

Abstract

fetched live from OpenAlex

This paper presents the results of a laboratory study on the effectiveness of the coagulation process in removing surfactants from water. The application of traditional coagulants (aluminium sulfate and iron chlorides) has not brought satisfactory results, the reduction in anionic surfactant (AS) content reached 7.6% and 10%, respectively. Adding cationic polyelectrolyte (Zetag-50) increased the removal efficiency to 24%. Coagulation using a polyelectrolyte alone proved to be more efficient, the reduction in surfactant content fluctuated at a level of about 50%. Complete surfactant removal was obtained when powdered activated carbon was added 5 minutes before the basic coagulant to the coagulation process. The efficiency of surfactant coagulation also increased after the application of powdered clinoptilolite, but to a smaller degree. Then the removal of AS was found to be improved by dosing powdered clinoptilolite simultaneously or with short delay after the addition of the basic coagulant.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.203
Teacher spread0.198 · 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