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Record W2016308680 · doi:10.1080/10643380801910090

Bioremediation and Phytoremediation of Pesticides: Recent Advances

2009· article· en· W2016308680 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCritical Reviews in Environmental Science and Technology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBioremediationPhytoremediationPollutantPesticideMicroorganismEnvironmental remediationBiodegradationBiotechnologyBiologyBiochemical engineeringContaminationBacteriaEcologyEngineering

Abstract

fetched live from OpenAlex

The use of genetically modified or native microorganisms and plants to degrade or remove pollutants has emerged as a powerful technology for in situ remediation. An understanding of the genetic basis of the mechanisms of how microorganisms and plants biodegrade pollutants and how they interact with the environment is important for successful implementation of this technology. Recent studies have demonstrated that microbes and transgenic plants produce pesticide-degradaing enzymes that can mineralize different groups of pesticides and their metabolites with greater efficiency. This review describes the most recent progress in biotechnological approaches for enhancing the capability of microorganisms and plants through the characterization and transfer of pesticide-degrading genes, induction of catabolic pathways, and display of cell surface enzymes.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
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 head0.012
GPT teacher head0.282
Teacher spread0.270 · 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