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Record W2793347592 · doi:10.21577/1984-6835.20180007

Diagnosis of Contamination of Soil by Toxic Metals from Urban Solid Waste and Influence of Organic Matter

2018· article· en· W2793347592 on OpenAlexaff
Maria Aparecida Liberato Milhome, Jayme Welton B. Holanda, José Ribeiro de Araújo Neto, Ronaldo Ferreira do Nascimento

Bibliographic record

VenueRevista Virtual de Química · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsOrganic matterContaminationEnvironmental chemistryEnvironmental scienceMunicipal solid wasteSoil contaminationHeavy metalsSoil organic matterWaste managementEnvironmental engineeringSoil scienceSoil waterChemistryEngineeringEcologyBiology

Abstract

fetched live from OpenAlex

In recent years, the disposal of urban solid waste (USW) in inadequate places has been increasing in Brazil. The objective of this study was to evaluate the contamination of lead, cadmium, copper, manganese, chromium, iron, nickel, cobalt and zinc metals and their relationship with organic matter (OM) in the vicinity of the city's "dump" Iguatu, Cear. Eight soil samples were collected in the layer from 0 to 0.20 m depth. The pH was carried out in distilled water, the OM by wet oxidation with potassium dichromate. The extraction of metals was carried out via acid digestion and its quantification by atomic absorption spectrometry. The results were compared with the soil guiding values established by CONAMA Resolution 430/2009. Analysis of the soil samples showed levels of Cr <Mn <Pb <Cu <Zn <Fe. Pearson's correlation showed that Zn, Mn and Fe were related to soil organic matter. Cu, Zn and Pb values were found at levels above that established by legislation, creating a risk of contamination in the region.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.227
Teacher spread0.221 · 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 designBench or experimental
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

Citations7
Published2018
Admission routes1
Has abstractyes

Explore more

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