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Record W2766490689 · doi:10.15626/eco-tech.2014.058

Phytoremediation of petroleum hydrocarbons contaminated soils with alfalfa (Medicago sativa)

2017· article· en· W2766490689 on OpenAlexaff
Charlotte Marchand, Fábio Kaczala, Yahya Jani, William Hogland

Bibliographic record

VenueLinnaeus Eco-Tech · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPhytoremediationMedicago sativaEnvironmental scienceEnvironmental remediationContaminationPetroleumSoil contaminationSoil waterPollutantBiomass (ecology)AmendmentTotal petroleum hydrocarbonWaste managementEnvironmental chemistryAgronomyChemistryBiologyEcology

Abstract

fetched live from OpenAlex

Underground storage tanks uses for waste cars draining contain many hazardous materials including hydrocarbons. These compounds pose a significant threat to the environment and affect negatively the health of living. Phytoremediation is an environmental friendly method used during the last few decades to eliminating organics pollutants from soil, sediment and water. The remediation capability of alfalfa (Medicago sativa) to treat petroleum-contaminated soil from an old car scrap yard in Nybro, Sweden was further investigated using greenhouse pot-scale experiments. After five months, alfalfa survival capacity and dry biomass were significantly lower in contaminated soil (CS) in comparison to non-contaminated soil (NCS). Only 5% of plants survived in CS and petroleum hydrocarbon C10-C50 content in planted treatment were not statistically lower in comparison to the unplanted treatment. Further studies are in process to evaluate the possible degradation of hydrocarbons using organic amendment

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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.

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

Citations4
Published2017
Admission routes1
Has abstractyes

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