MétaCan
Menu
Back to cohort
Record W2567369629 · doi:10.1021/acs.iecr.6b04092

Biopolymer Flocculants and Oat Hull Biomass To Aid the Removal of Orthophosphate in Wastewater Treatment

2016· article· en· W2567369629 on OpenAlexafffund
Henry K. Agbovi, Lee D. Wilson, Lope G. Tabil

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of Saskatchewan
FundersMinistry of Agriculture - SaskatchewanUniversity of Saskatchewan
KeywordsAlumFlocculationChitosanChemistryBiopolymerTernary operationNuclear chemistryWastewaterChromatographyCoagulationAluminium sulfateTernary numeral systemPulp and paper industryOrganic chemistryWaste managementPolymer

Abstract

fetched live from OpenAlex

This study reports on the removal of orthophosphate (P i ) by coagulation–flocculation with variable combinations of alum, biopolymers, and biomass. The combinatorial effects of these coagulant aids were evaluated for single, binary, and ternary systems. The role of pH, component dosages, and P i concentration on the coagulation–flocculation efficacy was evaluated. There was an optimal dosage of alum (30 mg/L) while alginate and chitosan were 15 mg/L. P i removal was 86% for alum and 98% for ternary systems containing chitosan and alginate where [P i ] = 10–11 mg P i /L. P i removal for the alum–alginate–chitosan ternary system was more efficient than that for the binary systems, especially at pH 6–7, where reduced efficiency occurred at pH > 7.5. P i removal was independent of concentration except at lower levels, [P i ] < 10 mg/L. The alum–refined oat hull binary system was 99% effective for P i removal, especially when [P i ] = 25 mg/L, with greater removal over the use of oat hulls alone.

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)0.001
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 score0.040
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.048
GPT teacher head0.282
Teacher spread0.234 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations26
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueIndustrial & Engineering Chemistry ResearchSame topicPhosphorus and nutrient managementFrench-language works237,207