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Record W2517121736 · doi:10.1039/c6ra16383a

Effective adsorption of phenolic pollutants from water using β-cyclodextrin polymer functionalized Fe<sub>3</sub>O<sub>4</sub> magnetic nanoparticles

2016· article· en· W2517121736 on OpenAlexaff
Tao Gong, Yehong Zhou, Linlin Sun, Wenting Liang, Jun Yang, Shaomin Shuang, Chuan Dong

Bibliographic record

VenueRSC Advances · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsWestern University
FundersShanxi Scholarship Council of ChinaNational Natural Science Foundation of China
KeywordsAdsorptionCyclodextrinPolymerPollutantNanoparticleMagnetic nanoparticlesChemistryChemical engineeringMaterials scienceOrganic chemistryNanotechnology

Abstract

fetched live from OpenAlex

β-Cyclodextrin polymer functionalized magnetic nanoparticles possess adsorption properties favorable for the purpose of removing phenolic pollutants.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.187
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.220
Teacher spread0.210 · 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; both teacher heads agree on what is shown here.

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

Citations47
Published2016
Admission routes1
Has abstractyes

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