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Record W4361190711 · doi:10.1128/spectrum.04448-22

Legacy Effects of Phytoremediation on Plant-Associated Prokaryotic Communities in Remediated Subarctic Soil Historically Contaminated with Petroleum Hydrocarbons

2023· article· en· W4361190711 on OpenAlexaff
Jakub Papík, Michal Strejcek, Lucie Musilová, Rodney Guritz, Mary‐Cathrine Leewis, Mary Beth Leigh, Ondřej Uhlík

Bibliographic record

VenueMicrobiology Spectrum · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersNational Institute of General Medical SciencesMinisterstvo Školství, Mládeže a TělovýchovyNational Institutes of HealthNational Science Foundation
KeywordsPhytoremediationSubarctic climateEcological successionVegetation (pathology)RhizosphereEnvironmental scienceEcosystemPlant communityEcologyEnvironmental remediationBiologySoil waterAgronomyContamination

Abstract

fetched live from OpenAlex

Subarctic ecosystems provide key services to local communities, yet they are threatened by pollution caused by spills and disposal of petroleum waste. Finding solutions for the remediation and restoration of subarctic soils is valuable for reasons related to human and ecosystem health, as well as environmental justice. This study provides novel insight into the long-term succession of soil and plant-associated microbiota in subarctic soils that had been historically contaminated with different sources of PHCs and subjected to distinct phytoremediation strategies. We provide evidence that even after the successful removal of PHCs and the occurrence of secondary succession, the fingerprint of the original source of contamination and the initial choice of remediation strategy can be detected as a microbial legacy in the rhizosphere, roots, and shoots of volunteer vegetation even 2 decades after the contamination had occurred. Such information needs to be borne in mind when designing and applying restoration approaches for PHC-contaminated soils in subarctic ecosystems.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.182
Teacher spread0.176 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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