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Record W1591467430 · doi:10.4137/aswr.s22465

Distribution of Contaminated Soils along Transversal and Longitudinal Gradients in Dynamic Fluvial Environment (Southern Québec, Canada)

2015· article· en· W1591467430 on OpenAlexaffabout
Diane Saint‐Laurent, Francis Baril, Ilias Bazier, Vernhar Gervais-Beaulac, Camille Chapados

Bibliographic record

VenueAir Soil and Water Research · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversité LavalMinistère de l'Agriculture, des Pêcheries et de l'AlimentationInnovation and Economic Development Trois RivièresUniversité du Québec à Trois-RivièresGDG Environnement
Fundersnot available
KeywordsSoil waterFlood mythAlluviumFluvialHydrology (agriculture)FloodplainAlluvial soilsSpatial distributionGeologyContaminationEnvironmental scienceAlluvial fanPhysical geographyGeomorphologySoil scienceGeographyArchaeologyEcologyGeotechnical engineeringCartography

Abstract

fetched live from OpenAlex

This research combines a hydrological and pedological approach to better understand the spatial distribution of contaminated soils along the Massawippi River (southern Québec, Canada). This river crosses through former mines, which were some of the largest copper mining areas in North America from 1865 to 1939. To determine the spatial distribution and concentration of the metal elements, soil samples were taken in each flood recurrence zone appearing on official flood zone maps. The maximum values obtained for Cu and Pb are 380 and 200 mg kg −1 , respectively, for the soils in the frequent flood zones (FFzs), while the values for soils in the moderate flood zones (MFzs) range from 700 to 540 (Cu) and 580 to 460 mg kg −1 (Pb). Contamination extends through several kilometers of the former mining sites (Eustis and Capleton), and concentration of metals in alluvial soils is slightly higher near the mine sites.

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)0.001
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 score0.022
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.026
GPT teacher head0.257
Teacher spread0.231 · 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

Citations1
Published2015
Admission routes2
Has abstractyes

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