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Geochemical behavior of heavy metals in differents environments in Rodrigo de Freitas lagoon - RJ/Brazil

2011· article· en· W1997914817 on OpenAlexfundno aff
Estefan Monteiro da Fonseca, José Antônio Baptista Neto, J.J. McAlister, Bernard Smith

Bibliographic record

VenueAnais da Academia Brasileira de Ciências · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
FundersUniversidade Federal FluminenseConselho Nacional de Desenvolvimento Científico e TecnológicoQueen's UniversityCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsPollutionEnvironmental scienceBioavailabilityHeavy metalsExtraction (chemistry)Environmental chemistrySewageSedimentHydrology (agriculture)GeologyEnvironmental engineeringChemistryGeomorphologyEcologyGeotechnical engineeringBiology

Abstract

fetched live from OpenAlex

The accelerated urbanisation without a planning, brought several environmental problems to Rio de Janeiro coastal zone, especially in areas such as Rodrigo de Freitas lagoon, which receives a great amount of untreated sewage every day. To assess the nature, potentially sources and extent of heavy metal pollution in the lagoon, sediments from the surrounding streets, from the entrance of the main canal that drains to the lagoon and from the bottom of the lagoon were collected and analysed by a modified selective extraction procedure in order to study the geochemical partitioning and bioavailability of Zn, Cu, Cr, Ni and Pb in these three compartments. The present study verified an increase in the Cu, Pb and Zn concentrations in the north of the Rodrigo de Freitas lagoon. Despite the different levels of oxidation between the sediments accumulated in the streets and in the bottom of the lagoon, the geochemical partitioning of the heavy metals did not show any pattern of variation for the metals, except for the element Cu. No concentrations were found in the soluble phase of samples collected in the surfacial sediments of the lagoon, suggesting no bioavailability of heavy metals.

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)0.001
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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.283
Teacher spread0.245 · 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.

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

Citations15
Published2011
Admission routes1
Has abstractyes

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