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Record W2055477072 · doi:10.4137/aswr.s10314

Spatial Variability of Heavy Metal Contamination in Alluvial Soils in Relation to Flood Risk Zones in Southern Québec, Canada

2012· article· en· W2055477072 on OpenAlexafffundabout
Diane Saint‐Laurent, Vernhar Gervais-Beaulac, Francis Baril, Claudia Matteau, Jean-Sébastien Berthelot

Bibliographic record

VenueAir Soil and Water Research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsInnovation and Economic Development Trois RivièresGDG EnvironnementUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlood mythSoil waterEnvironmental scienceHydrology (agriculture)AlluviumRiparian zoneContaminationPollutionSedimentationFloodplainAlluvial soilsAlluvial fanSedimentGeologySoil scienceGeographyEcologyGeomorphology

Abstract

fetched live from OpenAlex

The contamination of rivers and riparian soils is a growing problem for several catchments in southern Quebec due to agricultural pollutants and other sources of pollution. This study deals with the concentration of heavy metals in alluvial soils and their spatial variability following the various flood return periods (0-20 years and 20-100 years) and outside flood zones. The heavy metal concentration of some soils exceeds levels in government standards. The elements with the highest concentrations are mainly Ni, Pb and Zn. For instance, the levels of Zn and Pb can be as high as 310 and 490 mg kg -1 . In general, heavy metal concentrations are highest in active sedimentation zones (0-20 yrs). In this instance, the ANOVA test was conducted to determine whether the differences in heavy-metal concentrations in the soils could be significant between the three zones (Frequent Flood (FF), Moderate Flood (MF), and No Flood (NF)). With the increase in the flood return rate and current hydroclimatic changes, a downstream remobilization of contaminants can be expected, leading to a larger riparian area of contamination.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2012
Admission routes3
Has abstractyes

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