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Record W2311471377 · doi:10.3390/w8040113

Salts Removal from Synthetic Solution-Potash Brine by Non-Planted Constructed Wetlands

2016· article· en· W2311471377 on OpenAlexafffund
Warawut Chairawiwut, Dena W. McMartin, Shahid Azam

Bibliographic record

VenueWater · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicConstructed Wetlands for Wastewater Treatment
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Regina
KeywordsPotashBrineSorbentSorptionEffluentChemistryPotassiumChloridePulp and paper industryEnvironmental chemistryEnvironmental scienceMineralogyEnvironmental engineeringAdsorption

Abstract

fetched live from OpenAlex

Four pilot-scale non-planted constructed wetlands (CWs) were employed to study the fate and transport of the two dominant chloride salts contained in brine resulting from solution mining activities in the potash industry. The simulated brine contained a 10:1 ratio of NaCl:KCl based on authentic brine characteristics. The multi-layer soils functioned as a main salt filtering component comprising of Regina Clay, sand and gravels. The CW systems were operated in three batches of 16 days (experiments 1–3). K+ ions were removed by 92% (4.6 mg/L) from the effluent, while Cl− and Na+ were removed in lower proportions of 51% (85.3 mg/L) and 45% (53.2 mg/L), respectively. Over time, the retained quantities of the three target salt ions decreased, indicating that clay sorption capacity may have been reached. This study demonstrated that Regina Clay has substantial sorbent capacity for salt ions contained in simulated potash brine solution.

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)0.000
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 head0.004
GPT teacher head0.178
Teacher spread0.175 · 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

Citations10
Published2016
Admission routes2
Has abstractyes

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