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Record W2744063616 · doi:10.4236/msce.2017.58002

Use of Industrial Coal Waste Materials as Adsorbents for Textile Effluent Remediation

2017· article· en· W2744063616 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Materials Science and Chemical Engineering · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsUniversity of Saskatchewan
FundersSaskPowerUniversities Space Research AssociationUniversity of Saskatchewan
KeywordsMaterials scienceAdsorptionLeaching (pedology)Methylene blueEnvironmental remediationEffluentPorosimetryWaste managementPoint of zero chargeFourier transform infrared spectroscopyNuclear chemistryPulp and paper industryCoalChemical engineeringPhotocatalysisOrganic chemistryContaminationComposite materialEnvironmental sciencePorosityChemistry

Abstract

fetched live from OpenAlex

This paper presents experimental study on six carbonaceous industrial waste samples that were obtained from a local industry in Saskatchewan, Canada. Hereafter, the samples are coded as ES1, ES2, ES3, PU, RPS and SS1 and were characterized using IR and 13C solid state NMR spectroscopy, nitrogen porosimetry, TGA, metal leaching analysis using ICP and point-of-zero-charge. Adsorption studies were conducted using two types of adsorptive dye probes (p-nitrophenol, PNP; and methylene blue; MB) at pH 4.60 and pH 7.00.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.245
Teacher spread0.216 · 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