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Record W2738656544 · doi:10.4236/gep.2017.56019

Chitosan Biopolymers for Analysis of Organic Acids in Aquatic Environments of Treatment Wetlands

2017· article· en· W2738656544 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.

Bibliographic record

VenueJournal of Geoscience and Environment Protection · 2017
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Saskatchewan
FundersNatural Resources Canada
KeywordsSorptionChitosanThermogravimetric analysisGlutaraldehydeChemistryPelletsMatrix (chemical analysis)Environmental chemistryPulp and paper industryChemical engineeringChromatographyOrganic chemistryAdsorptionMaterials science

Abstract

fetched live from OpenAlex

Herein, we report on the use of chitosan-based engineered materials for the sequestration of naphthenic acid fraction compounds (NAFCs) and other species (matrix) in oil sands process-affected water (OSPW) in order to improve monitoring of NAFCs after phytoremediation. Chitosan pellets (CPs) were cross linked with glutaraldehyde (GLU) at variable feed ratios and characterized using thermogravimetric analysis (TGA). Sorption studies at equilibrium and kinetic conditions were carried on OSPW extract, raw and treated wetland samples. The materials were shown to have similar sorption capacity for NAFCs but with variable selectivity of the species in the complex mixture. As well, the matrix uptake varied according to the type of OSPW. Overall, CP in its native form outperformed the cross linked CP pellets, as evidenced by a reduction in matrix effects.

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 score0.060
Threshold uncertainty score0.290

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)0.0000.000

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 head0.018
GPT teacher head0.247
Teacher spread0.229 · 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