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Record W2579668078 · doi:10.1080/15226514.2016.1278422

Soil trace element changes during a phytoremediation trial with willows in southern Québec, Canada

2017· article· en· W2579668078 on OpenAlexaffabout
François Courchesne, Marie‐Claude Turmel, Benoît Cloutier‐Hurteau, Gilbert Tremblay, Lara Munro, Jacynthe Masse, Michel Labrecque

Bibliographic record

VenueInternational Journal of Phytoremediation · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPhytoremediationLeaching (pedology)WillowSoil waterEnvironmental scienceAgronomyTrace elementChemistryEnvironmental chemistryBotanySoil scienceBiology

Abstract

fetched live from OpenAlex

This study determined the changes in trace elements (TE) (As, Cd, Cu, Ni, Pb, Zn) chemistry in the soils of a willow (“Fish Creek” – Salix purpurea, SV1 – Salix x dasyclados and SX67 – Salix miyabeana) plantation growing under a cold climate during a three-year trial. The soil HNO3-extractable and H2O-soluble TE concentrations and pools significantly decreased under most cultivars (Fish, SX67). Yet, TE changes showed inconsistent patterns and localized soil TE increases (Ni, Pb) were measured. Temporal changes in soil TE were also detected in control plots and sometimes exceeded changes in planted plots. Discrepancies existed between the amount of soil TE change and the amount of TE uptake by willows, except for Cd and Zn. Phytoremediation with willows could reduce soil Cd and Zn within a decadal timeframe indicating that they can be remediated by willows in moderately contaminated soils. However, the time needed to reduce soil As, Cu, Ni and Pb was too long to be efficient. We submit that soil leaching contributed to the TE decrease in controls and the TE discrepancies, and that the plantation could have secondary effects such as the accelerated leaching of soil TE.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.790

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 studies0.0000.000
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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

Explore more

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