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Record W2911844125 · doi:10.1080/15226514.2018.1523870

Kinetics of phytoremediation of petroleum hydrocarbon contaminated soil

2019· article· en· W2911844125 on OpenAlexafffundabout
Elizabeth W. Murray, Bruce M. Greenberg, Kent Cryer, Ben Poltorak, Justin McKeown, Jess Spies, Perry D. Gerwing

Bibliographic record

VenueInternational Journal of Phytoremediation · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsUniversity of WaterlooEarthmaster Environmental Strategies (Canada)
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsEnvironmental remediationPhytoremediationRhizosphereEnvironmental scienceSoil contaminationTotal petroleum hydrocarbonBiomass (ecology)ContaminationEnvironmental chemistryEnvironmental engineeringSoil scienceChemistrySoil waterAgronomyEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Earthmaster and the University of Waterloo have successfully developed plant growth promoting rhizobacteria (PGPR) - Enhanced Phytoremediation Systems (PEPSystems™) which have been deployed across Canada for the treatment of soil contaminated with petroleum hydrocarbons (PHCs), including CCME fractions F2 and F3. A challenge with phytoremediation is to predict the length of time to remediate a site so that site owners will be inclined to use the technology. In previous field trials of PEPSystems, it was determined that PHC was mostly degraded by microbes in the rhizosphere, following first-order exponential decay kinetics. Using new PEPSystems data collected from multiple commercial remediation sites across Western Canada, the kinetic equations of PHC decay were tested to determine if remediation time was accurately predicted. In general, when compared to the predicted time to remediation endpoint, data from recent commercial field applications showed that 35% and 20% less time was needed to reach remediation endpoints for fractions F2 and F3, respectively. As a result, the predictive kinetic equation for fraction F2 degradation was updated to reflect current remediation outcomes. Insufficient data were available to update the F3 equation. Being able to more accurately predict remediation timelines will enhance the value and utilization of PEPSystems.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.993

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)0.0010.000

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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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

Citations12
Published2019
Admission routes3
Has abstractyes

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