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Record W2162186315 · doi:10.5539/ep.v1n1p38

Heavy Metal and Phosphorus Removal from Waters by Optimizing Use of Calcium Hydroxide and Risk Assessment

2011· article· en· W2162186315 on OpenAlexvenueno aff
Binyuan Chen, Ruijuan Qu, Jiaqi Shi, Dinglong Li, Zhongbo Wei, Xi Yang, Zunyao Wang

Bibliographic record

VenueEnvironment and Pollution · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsChemistryCalcium hydroxidePhosphorusWastewaterCalcium carbonateEnvironmental chemistryCalciumEutrophicationAlkalinitySedimentationHydroxideDaphniaPollutantMetal hydroxideEnvironmental engineeringInorganic chemistrySedimentEnvironmental scienceNutrientEcologyZooplanktonGeology

Abstract

fetched live from OpenAlex

The optimizing using calcium hydroxide to remove dissolved heavy metal, phosphorus pollutants and algae was investigated. It was found that the concentration of calcium ion was minimal at pH 10.5 when a large amount of generated calcium carbonate increased the particle size of the precipitates and improved sedimentation of sludge and the removal efficiency of heavy metal and phosphorus significantly. Regardless of the initial heavy metals concentrations contained in the wastewater, the final treated concentrations were all extremely low. Risk assessment in alkaline environment of pH 10.5 was tested by fancy carp, daphnia, seed, luminescent bacterium Q67. The results showed that pH 10.5 had a little influence on the four tested organisms. Thus it is suggested that calcium hydroxide at pH 10.5 may be a potential method for treating wastewater and eutrophication water.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.018
GPT teacher head0.199
Teacher spread0.182 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2011
Admission routes1
Has abstractyes

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