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Record W3217515082 · doi:10.22158/se.v6n4p16

Contamination Assessment of Heavy Metals in Sediment Cores from De Montigny Lake around Siscoe-Sullivan Former Mining Sites, Val-d’Or, Canada

2021· article· en· W3217515082 on OpenAlexafffundabout
Patrick Vualu Ibula Mambenga

Bibliographic record

VenueSustainability in Environment · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
FundersMitacs
KeywordsSedimentEnrichment factorContaminationEnvironmental chemistryEnvironmental scienceHeavy metalsWater qualitySediment coreChemistryGeologyEcologyGeomorphology

Abstract

fetched live from OpenAlex

Seven sediment cores were collected from De Montigny Lake in order to determine concentrations, and contamination assessment of heavy metals such as Cr, Zn, Ni, Pb, Cu, Co and Cd. The mean concentrations of heavy metals are as follows: 48.3 mg/kg for Cr, 36.4 mg/kg for Zn, 20.6 mg/kg for Ni, 14.7 mg/kg for Pb, 10.2 mg/kg for Cu, 6.7 mg/kg for Co and 0.1 mg/kg for Cd. Based on the sediment quality guidelines, the mean concentration metals such as Cr, Cu and Ni exceeded the US Environmental Protection Agency (USEPA) guideline. However, the concentration of Cr was more than the Canadian Water Quality Guidelines for Protection of Aquatic Life (CCME), and Threshold Effect Level (TEL) guidelines. The metal contamination in the sediments was also evaluated using Enrichment Factor (EF) and geoaccumulation index (Igeo) to assess natural and anthropogenic factors. The results of enrichment factor methods demonstrated that sediments from De Montigny Lake were moderately to high enriched, mainly controlled by through anthropogenic activities. According to Sediment Quality Guidelines (SQGs), the concentrations metals from the core sediment of De Montigny Lake are classified as having moderate impacts with potential adverse biotoxic effects.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.012
GPT teacher head0.265
Teacher spread0.253 · 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

Citations0
Published2021
Admission routes3
Has abstractyes

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