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Record W1997550270 · doi:10.1080/15226510902717606

Phytoremediation of Metal-Contaminated Soil in Temperate Humid Regions of British Columbia, Canada

2009· article· en· W1997550270 on OpenAlexaffabout
Prabha K. Padmavathiamma, Loretta Y. Li

Bibliographic record

VenueInternational Journal of Phytoremediation · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLolium perenneAgronomyPhytoremediationFestuca rubraHelianthus annuusFestucaSowingPoa pratensisFestuca pratensisPerennial plantSoil waterFestuca arundinaceaTemperate climateSunflowerLoliumBioconcentrationBrassicaBiologyEnvironmental scienceBotanyBioaccumulationPoaceaeEcology

Abstract

fetched live from OpenAlex

The suitability of five plant species was studied for phytoextraction and phytostabilisation in a region with temperate maritime climate of coastal British Columbia, Canada. Pot experiments were conducted using Lolium perenne L (perennial rye grass), Festuca rubra L (creeping red fescue), Helianthus annuus L (sunflower), Poa pratensis L (Kentucky bluegrass) and Brassica napus L (rape) in soils treated with three different metal (Cu, Pb, Mn, and Zn) concentrations. The bio-metric characters of plants in soils with multiple-metal contaminations, their metal accumulation characteristics, translocation properties and metal removal were assessed at different stages of plant growth, 90 and 120 DAS (days after sowing). Lolium was found to be suitable for the phytostabilisation of Cu and Pb, Festuca for Mn and Poa for Zn. Metal removal was higher at 120 than at 90 days after sowing, and metals concentrated more in the underground tissues with less translocation to the aboveground parts. Bioconcentration factors indicate that Festuca had the highest accumulation for Cu, Helianthus for Pb and Zn and Poa for Mn.

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 score0.423
Threshold uncertainty score0.818

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 studies0.0000.000
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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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

Citations32
Published2009
Admission routes2
Has abstractyes

Explore more

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