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Record W2193618338 · doi:10.32360/acmar.v44i2.164

AVALIAÇÃO DAS CONCENTRAÇÕES DE METAIS PESADOS EM SEDIMENTOS DO ESTUÁRIO DO RIO TIMBÓ, PERNAMBUCO-BRASIL

2011· article· pt· W2193618338 on OpenAlexaboutno aff
Tibério Jorge Melo de Noronha, Hélida Karla Philippini da Silva, Marta Maria Menezes Bezerra Duarte

Bibliographic record

VenueArquivos de Ciências do Mar · 2011
Typearticle
Languagept
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsEstuaryEnvironmental chemistryEnvironmental scienceHydrology (agriculture)SedimentManganeseWastewaterEffluentPollutionChemistryEnvironmental engineeringGeologyEcologyOceanography

Abstract

fetched live from OpenAlex

Timbo River Estuary is located in the Metropolitan Region of Recife, among the cities Paulista, Abreu e Lima and Igarassu. It has an approximate area of 1397 hectares, and is affected by human action, especially that related to urban pressure and industrial activities. This work aims to determine the hydrological parameters and heavy metals concentrations (Zn, Mn, Cr, Cu, Ni, Cd and Fe), in rainy and dry periods. Hydrological parameters were measured at four points in the main channel of the river, according to Standard Methods for Examinations of Water and Wastewater. Sediment samples were submitted to acid process in a sample digester using the microwave and, for the quantification of metals, using the Optical Emission Spectrometer with Inductively Accolade Plasma (ICPOES). Results indicated that Timbo water is compromised concerning dissolved oxygen values (DO); and concerning dissolved oxygen saturation which indicated oversaturation in the most stations sampled. The maximum values of zinc (723 mg.kg-1), manganese (438 mg.kg-1), chrome (338 mg.kg-1) and iron (62840 mg.kg-1) were above those of reference, also presenting concentrations higher than the guide-values adopted by the Canadian Counselor of Environmental Minister. However, pH values (reduced conditions) and the organic load tend to immobilize metals in the sediment by adsorption and precipitation mechanisms making them less available to the other aquatic compartments.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0450.008

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 head0.044
GPT teacher head0.292
Teacher spread0.248 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2011
Admission routes1
Has abstractyes

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