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Record W4392925945 · doi:10.53623/tasp.v4i1.403

Biodegradation of Chlorpyrifos by Microbes: A Review

2024· review· en· W4392925945 on OpenAlexafffund
Rubiyatno Rubiyatno, Aulia Maulana, Jovale Vincent Tongco, Arma Yulisa, Sang Hyeok Park, Md Abu Hanifa Jannat, Rega Permana, Ocean Thakali, Michael Lie, Aouatif Fahssi, Ouahiba Aziez, Camilo Bastidas

Bibliographic record

VenueTropical Aquatic and Soil Pollution · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsUniversity of Ottawa
FundersUniversitas PadjadjaranUniversidad de CantabriaPohang University of Science and TechnologyUniversity of Ottawa
KeywordsBiodegradationChlorpyrifosEnvironmental chemistryChemistryMicrobiologyBiologyEcologyPesticideOrganic chemistry

Abstract

fetched live from OpenAlex

Chlorpyrifos (CP) is a widely used organophosphate pesticide known for its recalcitrant nature, raising concerns about potential ecological and health impacts due to its toxicity. Many plants and animals are contaminated with this pesticide. Microbial biodegradation offers an environmentally friendly and effective method to remove CP from the environment and mitigate its impacts, especially given its low cost, particularly when bioremediation is conducted on-site. Different types of microbial species have been found to function under various environmental conditions, with some, like Pseudomonas nitroreducens PS-2 and Pseudomonas aeruginosa (NCIM 2074), showing promising results with degradation rates of up to 100%. However, challenges exist, such as partial degradation caused by the presence of metabolites, and the recalcitrant nature of CP, which can impede microbes' ability to effectively degrade its hydrocarbon ring. Overall, a combination of approaches, such as microbial and algal methods, or the discovery of new microbial strains, can help overcome these challenges and further enhance the long-term viability of this technique.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.001

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 head0.022
GPT teacher head0.279
Teacher spread0.258 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2024
Admission routes2
Has abstractyes

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