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Record W2766509081 · doi:10.5539/jas.v9n11p190

Dynamics of Ions in Soils Irrigated with Saline Reject

2017· article· en· W2766509081 on OpenAlexvenueno aff
ANDLER MILTON PAIVA DE OLIVEIRA, Cezar Augusto Medeiros Rebouças, Nildo da Silva Dias, Francisco Souto de Sousa Júnior, Francisco Vaniés da Silva Sá, Osvaldo Nogueira de Sousa Neto, Alexandre de Oliveira Lima, Cleyton dos Santos Fernandes

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsnot available
Fundersnot available
KeywordsCambisolBrackish waterChemistrySoil waterOxisolEnvironmental chemistryIonSoil scienceEnvironmental scienceSalinityGeology

Abstract

fetched live from OpenAlex

The objective of this work was to evaluate the variation in the electrical conductivity and the mobilization of ions present in reject brine from desalination plant of brackish well water in three types of soil. The mobilization of the contaminant ions in the reject brine was studied in glass percolation columns, which were filled with soil of contrasting textures (eutrophic CAMBISOL, typic dystrophic Red OXISOL, ENTISOL Quartzipsamment). Experiments ware repeated three times each, and the initial and final concentrations of the ion contaminants were analyzed. The pollution potential of this residue was determined by the retardation factor and dispersion-diffusion coefficient of Ca2+ and Mg2+, besides the variation of electrical conductivity along the profile of each soil studied. In the Red Oxisol, Ca2+ and Mg2+ ions move with greater ease resulting in possibility largest of potential of ground water contamination. In Entisol Quartzipsamment presented higher Mg2+ (R) ion advancement speed, that is, higher subsurface contamination power for these ions. The eutrophic Cambisol presented low diffusion-dispersion coefficient in all the evaluated ions and, therefore, lower mobility of the ions in the soil profile, and consequently, a greater possibility of contamination when irrigated with reject brine.

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 score0.380
Threshold uncertainty score0.251

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 communication0.0000.001
Open science0.0010.000
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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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