MétaCan
Menu
Back to cohort
Record W2060199738 · doi:10.1002/cjce.21620

Use of surfactants and blends to remove DDT from contaminated soils

2011· article· en· W2060199738 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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Chemistry and Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPulmonary surfactantChemistryLeaching (pedology)Soil waterContaminationEnvironmental chemistryBenzeneChromatographySodiumChemical engineeringOrganic chemistryEnvironmental scienceSoil science

Abstract

fetched live from OpenAlex

Abstract Removal of dichloro‐diphenyl‐trichloroethane (DDT) from soils using surfactant‐enhanced solubilisation was studied both in batch and continuous flow arrangements to determine if there were advantages to using a combination of non‐ionic (Tween and Brij) and anionic surfactants. It was observed that the presence of the anionic surfactant sodium dodecyl benzene sulphonate improved the DDT removal efficiency, but had a potentially negative effect on flow rates in column leaching experiments at concentrations over 0.1%. The potential for re‐use of the surfactant mixture was studied and demonstrated by removing DDT and its metabolites from the surfactant solution using activated carbon. © 2011 Canadian Society for Chemical Engineering

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 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 score0.007
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.161
Teacher spread0.147 · 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