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Análisis de aguas superficiales con alto contenido de fosfatos para el diseño de una planta de tratamiento de agua potable

2020· article· es· W3045706476 on OpenAlexvenueno aff
Ana Gabriela Flores Huilcapi, Luis Santiago Carrera Almendáriz, Carlos Alcíbar Medina Serrano

Bibliographic record

VenueConcienciaDigital · 2020
Typearticle
Languagees
FieldEnvironmental Science
TopicWater Resource Management and Quality
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPotable waterPhysicsChemistryPhilosophyEnvironmental engineeringEnvironmental science

Abstract

fetched live from OpenAlex

El objetivo de esta investigación es el diseño de una planta de tratamiento de agua potable a partir de aguas superficiales. El muestreo del agua cruda de la captación se realiza sistemáticamente durante cuatro semanas consecutivas; las muestras tomadas fueron caracterizadas en un laboratorio de control de calidad mediante pruebas físico-químicas y microbiológica según la Norma Técnica INEN 1108:2014 referida a Requisitos de Agua Potable. Se identifica que las muestras de agua contienen concentraciones de fosfatos y turbidez fuera de los límites permisibles según normativa vigente, para turbiedad 5 NTU y para fosfatos 0,1 mg/L. Para disminuir la concentración de fosfatos de desarrollan pruebas de tratabilidad a nivel de laboratorio, realizando dosificaciones de sulfato de cobre en solución por cada litro de agua cruda. La adición de 5 mg/L de sulfato de cobre disminuye la concentración de fosfatos en 82, 5 %, parámetro que está dentro de la norma establecida. A partir de un caudal máximo de tratamiento de 24L/s de captación de agua superficial se realizan cálculos de ingeniería y diseño para la planta de tratamiento de agua potable que contiene un medidor de caudales, dos sedimentadores, dos filtros ascendentes gruesos y tres filtros lentos con arena fina. El medidor de caudales es tipo parshall con un ancho de garganta de W = 0,229; el sedimentador clásico con una velocidad de sedimentación crítica Vsc = 0,26 mm/s; el filtro ascendente grueso con una velocidad de filtración de Vf = 0,6 m/h y el filtro lento con una velocidad de filtración de Vf = 0,3 m/h.

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)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.273
Teacher spread0.234 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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