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Record W1963541575 · doi:10.1080/09593330801984365

THE MEASUREMENT OF MAGNESIUM: A POSSIBLE KEY TO STRUVITE PRODUCTION AND PROCESS CONTROL

2008· article· en· W1963541575 on OpenAlexaff
Alexander L. Forrest, D. S. Mavinic, Frank-Thomas Koch

Bibliographic record

VenueEnvironmental Technology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStruviteMagnesiumProduction (economics)Process (computing)Key (lock)Environmental scienceProcess engineeringChemistryPulp and paper industryMaterials scienceMetallurgyEngineeringComputer science

Abstract

fetched live from OpenAlex

Struvite, a crystalline structure comprised of ions of magnesium (Mg2+), ammonium (NH4-N) and phosphate (PO4-P), is commonly encountered in wastewater treatmentplants (WWTPs) through struvite encrustation. The gradual growth of this crystal can lead to h igh maintenance costs, due to downtime and replacement parts. Several struvite recovery unit processes have been developed in an effort to reduce this problem, through the preferential removal of the constituent ions (Mg2+, NH4-N, and PO4-P) upstream of problem areas (e.g. anaerobic digester supernatants). One of the key elements of process control for these systems is accurately determining the constituent concentrations. Although a wide variety of measurement techniques exist for both NH4-N and PO4-P, the presence of PO4-P interferes with the measurement of Mg2+. Ion selective electrodes (ISEs) were tested on wastewater samples to determine Mg2+ concentrations. It was found that the two ISE tested produced unreliable results, as they both proved non-specific t o Mg2+. A modification, using polyaluminum chloride (PAC), was developed to remove the interference of phosphates from the colorimetric technique. It was found to produce reliable results within 10% of those results predicted by atomic absorption. The resulting technique averaged about 10 minutes per sample and could be conducted inexpensively at a laboratory facility at WWTPs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.392

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 communication0.0000.000
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 head0.006
GPT teacher head0.180
Teacher spread0.174 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

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