MétaCan
Menu
Back to cohort
Record W4300865307 · doi:10.1680/jenes.22.00020

The effect of soil types on the phytoremediation of heavy metals by <i>Phragmites australis</i>

2022· article· en· W4300865307 on OpenAlexvenueno aff
Samuel Wiafe, Sarah Fanny Hackman Duncan, Boakye Ebenezer, Samuel Yeboah Baako

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsPhragmitesLoamPhytoremediationEnvironmental remediationSoil waterBioaccumulationMercury (programming language)Soil classificationChromiumAgronomyEnvironmental chemistryChemistryEnvironmental scienceSoil scienceWetlandContaminationBiologyEcology

Abstract

fetched live from OpenAlex

The remediation of heavy metals from contaminated sites by the application of phytoextraction is proving to be efficient and cost-effective. A pot experiment was conducted to ascertain the effect of soil types (sand, loam and clay) in the remediation of copper (Cu), chromium (Cr) and mercury (Hg) using Phragmites australis. The results obtained throughout 60 days of plant growth showed that the copper absorption in the roots of the plant in sandy, loamy and clayey soils was 47, 79.1 and 96 mg/kg, respectively. Chromium absorption in the roots of the plant in sandy, loamy and clayey soils was 136, 180 and 353 mg/kg, respectively, while mercury absorption in sand, loam and clay was 11.7, 14.8 and 19.3 mg/kg. The translocation factor for all metals in the three soil types was less than 1; however, the bioaccumulation factor in all cases was more than 1. The study concluded that the accumulation of chromium in the tissues of the plant was more pronounced than those of copper and mercury. Clayey soil proved to be the favourable soil required for the effective remediation of the metals into the tissues of a plant.

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)0.003
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.004
GPT teacher head0.187
Teacher spread0.183 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Environmental Engineering and ScienceSame topicHeavy metals in environmentFrench-language works237,207