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Record W2724566043 · doi:10.32360/acmar.v44i2.158

AVALIAÇÃO PRELIMINAR DAS CONCENTRAÇÕES DE METAIS PESADOS NOS SEDIMENTOS DA LAGOA DO ARAÇÁ, RECIFE, ESTADO DE PERNAMBUCO

2011· article· pt· W2724566043 on OpenAlexaboutno aff
Josineide Braz de Miranda, Hélida Karla Philippini da Silva, Erika Cristina Ferreira da Silva, Marta Maria Menezes Bezerra Duarte

Bibliographic record

VenueArquivos de Ciências do Mar · 2011
Typearticle
Languagept
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceEnvironmental chemistryManganeseContaminationPollutionSedimentZincLimitingChemistryMetallurgyGeologyEcologyBiologyMaterials science

Abstract

fetched live from OpenAlex

The mangroves have been suffering an intense process of environment degradation due to the amount of untreated residues discharged into effluents, principally by trace metals, thus causing contamination. The Araca Lagoon is formed by a mangrove ecosystem, which is situated at the Recife, Pernambuco, and it is part of an the Environment Preservation Area. However, a number of anthropic activities have been contributing to its environment contamination. Therefore, this research has the objective of making a preliminary assessment of the impact on the Araca Lagoon by such trace metals as chrome (Cr), iron (Fe), manganese (Mn) and zinc (Zn). In the sediment the manganese, zinc and chrome contents presented concentration levels above the limiting values for soils of Sao Paulo State (CETESB), and quality guides values of sediments in Canada (CCME). The iron content was compared with the limit established by the Environmental Protection Agency of the United States (USEPA) and its concentration in all stations was above the recommended limit value. The Araca Lagoon sediments presented contamination by chrome, iron, manganese and zinc and thus presents itself as an important local accumulator of these trace 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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient 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 score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0290.007

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 head0.036
GPT teacher head0.273
Teacher spread0.237 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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