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Phytoremediation Enhancement of Natural Attenuation Processes

2019· other· en· W3020835268 on OpenAlexaboutno aff
David B. Vance, James A. Jacobs

Bibliographic record

VenueEncyclopedia of Water · 2019
Typeother
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsPhytoremediationBioremediationEnvironmental remediationRhizosphereEnvironmental sciencePhytoextraction processSoil contaminationContaminationAgronomyHyperaccumulatorBiologySoil waterEcologySoil science

Abstract

fetched live from OpenAlex

Abstract Phytoremediation is the use of living green plants for in situ remediation of contaminants using what is in essence enhanced natural attenuation for soil and groundwater cleanup. There are a variety of phytoremediation methods, some having multiple names: phytostabilization, rhizodegradation (phytostimulation, rhizosphere bioremediation, or plant‐assisted bioremediation), rhizofiltration (contaminant uptake), phytodegradation (phytotransformation), phytovolatilization, and phytoaccumulation (phytoextraction or hyperaccumulation). Various types of plants can be used in phytoremediation, including poplar trees, alfalfa, black locust, Indian mustard, fescue grass, crested wheatgrass, and Canada wild rye. This article reviews phytoremediation methods as applied to organic contaminants and heavy metals.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.065
Threshold uncertainty score0.999

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)0.0110.002

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 head0.215
Teacher spread0.210 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2019
Admission routes1
Has abstractyes

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