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Record W3036692866 · doi:10.1680/jenes.19.00059

Modified sand for the removal of manganese and arsenic from groundwater

2020· article· en· W3036692866 on OpenAlexvenueno aff
Md. Ehosan Habib, Muhammad Ashraf Ali, Kazi Parvez Fattah

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsManganeseArsenicChemistryPhosphateEnvironmental chemistryInorganic chemistryNuclear chemistry

Abstract

fetched live from OpenAlex

Manganese (Mn) and arsenic (As) are common natural groundwater contaminants and are present at concentrations much higher than the recommended drinking water guidelines. In this study, laboratory investigations were carried out to evaluate the effectiveness of three different types of manganese oxide (MnO 2 )-coated media in removing manganese and arsenic from water. While all media types were found to be very effective in removing dissolved manganese, the flow rate or contact time was found to have a significant impact on manganese removal. Removal of manganese with ‘synthetic’ manganese-coated media changed from 30% at a flow rate of 8.0 (ml/min)/cm 2 to almost 100% at flow rates of about 1 (ml/min)/cm 2 . Manganese removal was found to increase with increasing manganese content of the filter media and with increasing manganese in the influent water. Removal of arsenic (III) (As 3+ ) in manganese oxide-coated filter media was found to be strongly dependent on pH and phosphate (PO 4 3− ) concentration. Removal of arsenic decreased from about 80% at pH 7 to almost nil at pH 9. Arsenic removal decreased from 75% in the absence of phosphate to 2.3% in the presence of 10 mg/l phosphate. Results show that manganese oxide-coated media can efficiently remove both manganese and arsenic.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.144

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 head0.185
Teacher spread0.176 · 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 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

Citations10
Published2020
Admission routes1
Has abstractyes

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