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Record W929180108 · doi:10.5935/rvq.v7i6.988

Caracterização Geoquímica Orgânica e Inorgânica de Sedimentos de Manguezais do Estuário São Francisco, Sergipe

2015· article· pt· W929180108 on OpenAlexaboutno aff
Jandyson M. Santos, Luana Oliveira dos Santos, José Arnaldo Santana Costa, Luciano Carlos Sobral de Menezes, Francisco Sandro Rodrigues Holanda, Iramaia Corrêa Bellin

Bibliographic record

VenueRevista Virtual de Química · 2015
Typearticle
Languagept
FieldEnvironmental Science
TopicGeography and Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceGeographyForestry

Abstract

fetched live from OpenAlex

Ecossistemas de manguezais se desenvolvem em regioes litorâneas e possuem caracteristicas intrinsecas, sendo no Brasil considerados areas de preservacao ambiental, porem, impactos antropogenicos associados a especulacao imobiliaria e atividades petroliferas estao alterando o cenario desses ecossistemas. O presente trabalho objetivou realizar a caracterizacao geoquimica orgânica e inorgânica (pH em agua, teor de materia orgânica (MO), analise elementar, determinacao de metais – Al, Na, K, Fe, Cd, Cr, Cu, Ni e Pb) de sedimentos de manguezais do Estuario Sao Francisco, Sergipe. Caracteristicas mineralogicas definiram os sedimentos como de acidez elevada (pH 10 e % MO superiores a 15 % estao relacionados a proximidade das estacoes de coleta a centros urbanos e industriais, caracteristicas intrinsecas da vegetacao nativa e diferentes aportes de biomassa. Os metais Cd, Cu e Ni, em alguns sedimentos, se mostraram acima dos limites referenciados por Threshold Effect Level ( TEL ) e Probable Effect Level (PEL) do Canadian Sediment Quality Guidelines for the Protection of Aquatic Life e do Conselho Nacional do Meio Ambiente (CONAMA), estando associados a atividades antropogenica na regiao e alertando para fins toxicologicos. DOI: 10.5935/1984-6835.20150126

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)0.002
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 score0.339
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.004

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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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
Published2015
Admission routes1
Has abstractyes

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