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Record W2008838175 · doi:10.1504/ijewm.2014.066592

Removal of phosphorus and residual aluminium with the simultaneous use of chitosan and alum on the effluent of an MBBR biological system during start-up

2014· article· en· W2008838175 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.

Bibliographic record

VenueInternational Journal of Environment and Waste Management · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAlumChitosanEffluentAluminiumPhosphorusAluminium sulfateChemistryResidualEnvironmental scienceEnvironmental chemistryPulp and paper industryWaste managementEnvironmental engineeringBiochemistryEngineeringOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

Physico–chemical treatment with aluminium salts is a common practice to remove total phosphorus (TP) from wastewaters. However, the use of alum can increase the residual aluminium concentration both in the effluent and biosolids. Chitosan, an alternative coagulant, does not allow for the removal of TP below the requirement level when lower than the soluble phosphorus fraction of the water. Hence, simultaneous dosage of alum and chitosan solutions was evaluated on the effluent of a newly installed MBBR (moving bed biofilm reactor) system for residual TP and aluminium removal. At alum optimal dosage, the most effective chitosan solution generated: 1) an optimal dosage zone for which residual TP and aluminium concentrations were minimal; 2) maximal abatements for TP and aluminium that reached 35 and 85%, respectively, the concentrations observed with alum only. Also, the fraction of residual aluminium in the biosolids was increased, particularly from 84 to 98% at optimal chitosan dosage.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.303

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 head0.009
GPT teacher head0.186
Teacher spread0.177 · 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