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Record W2288760530 · doi:10.14288/1.0062593

The removal of heavy metals from municipal wastewaters by lime-magnesium coagulation

2010· article· en· W2288760530 on OpenAlexaboutno aff
Byard H. MacLean

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsnot available
Fundersnot available
KeywordsLimeMagnesiumCoagulationHeavy metalsWaste managementEnvironmental scienceChemistryPulp and paper industryEnvironmental chemistryMetallurgyEngineeringMaterials scienceMedicine

Abstract

fetched live from OpenAlex

The evidence of heavy metal build up in the aquatic environment near sewage treatment plant outfalls around Vancouver,coupled with the generally held theory that secondary treatment is not required in this area, leads to the conclusion that a treatment method is required that is primarily aimed at heavy metal removal. In this study, jar tests were performed to evaluate the heavy metal removal efficiency of the lime-magnesium coagulation process. Five heavy metals (Cr³⁺ , Cu²⁺ , Pb²⁺ , Ni²⁺ and Zn²⁺ ) were all tested at initial concentrations of .5, 2.5 and 5.0 mg/1 individually and in combination. The experiments were performed on prechlorinated primary effluent and raw sewage at the natural alkalinity levels (120-130 mg/1 as CaCO₃), and some work was done at elevated alkalinity (190-200 mg/1). The need for filtration in the process was also researched. Results of the study indicated that the heavy metal removal efficiency was enhanced by the presence of Mg²⁺ at a given lime dosage for all of the heavy metals except nickel. A comparison indicated that intermediate lime treatment (220 mg/1) coupled with 33 mg/1 Mg²⁺ might be a more attractive process than just straight high lime treatment (400 mg/1).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.731

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)0.0010.000

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 head0.004
GPT teacher head0.158
Teacher spread0.154 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

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