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Record W2943054359 · doi:10.5539/jas.v11n6p394

Characterization of Water Quality Indicators in the Micro-basin of the Arizona River, Atlántida (Honduras)

2019· article· en· W2943054359 on OpenAlexvenueno aff
Luis Antonio Beltrán Alemán, Ismael Montero-Fernández, Selvin Antonio Saravia Maldonado, Dixon Nohel Morales López, José David Portillo Villanueva, Nelson Edmundo Arriaga Pérez

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Resource Management and Quality
Canadian institutionsnot available
Fundersnot available
KeywordsWatershedWater qualityHydrology (agriculture)Principal component analysisGeographyDrainage basinSampling (signal processing)Water resource managementEnvironmental sciencePopulationMultivariate statisticsStructural basinGeographic information systemCartographyStatisticsGeologyEngineeringComputer scienceMathematicsEnvironmental healthEcology

Abstract

fetched live from OpenAlex

The water quality was studied in the basin of the Arizona River, supplier of the urban helmet of the municipality of Arizona, department of Atlántida (Honduras). In order to determine the quality of the water provided to the population. The methodology implemented consisted in carrying out tours in the area to obtain information on the delimitation, maps of land uses using tools of the geographic information system (GIS), then it was determined to assign the sampling sites for the three days for three months being the the following: take, storage tank and three taps of the urban center of the municipality of Arizona. The analytical parameters for the micro-watershed (site work) were evaluated and analyzed by the NSF ICA with a multivariate statistical analysis of principal component methods, the remaining analyzes were developed in comparisons according to the admissible values of the Standard Technique for the Quality of Drinking Water of Honduras 1995.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.215
Teacher spread0.207 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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