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Record W3011553155 · doi:10.2166/wqrj.2020.017

Preliminary investigation into the claims of the IBROM system

2020· article· en· W3011553155 on OpenAlexaffabout
Zahra Vojdani, Beata Gorczyca

Bibliographic record

VenueWater Quality Research Journal · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDissolved organic carbonChemistryReverse osmosisChloraminationFiltration (mathematics)Water treatmentBiofoulingFoulingEnvironmental chemistryMembrane foulingTrihalomethaneMembraneChlorineEnvironmental engineeringChloramineEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Membrane filtration is commonly applied to reduce dissolved organic carbon (DOC) to control the formation of trihalomethanes (THMs); however, high levels of DOC can cause severe fouling of reverse osmosis membranes. The integrated biological and reverse osmosis membrane (IBROM) process is a combination of biological filters and reverse osmosis membranes. The IBROM process claims to remove biodegradable dissolved organic carbon (BDOC), which apparently should result in reduced membrane fouling. The goal of this research was to conduct a preliminary investigation into the claims of the IBROM system, using water collected from the Herbert water treatment plant (Saskatchewan). The plant is utilizing the IBROM for the treatment of a dugout and groundwater blend (DOC of 17.5–22.7 mg/L). The results demonstrated that BDOC concentrations did not change significantly throughout the plant. Optimized laboratory-scale coagulation with polyaluminium chlorohydrate achieved 58% removal of BDOC. Oxidation with permanganate increased the concentration of BDOC (from 5.7 to 8.8 mg/L). Overall, BDOC was effectively removed by optimized coagulation rather than the IBROM system. Moreover, the results show an inverse relationship between BDOC and THMs formation potential (THMFP) in both coagulated and oxidized water. For all concentrations, more biodegradable DOC had less tendency to form THMs based on the lower THMFP.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.119
GPT teacher head0.343
Teacher spread0.224 · 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 designObservational
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

Citations2
Published2020
Admission routes2
Has abstractyes

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