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Record W2867741138 · doi:10.5539/jas.v10n8p107

Preservative Effects of Strobilurin Fungicides on Citrus Storage Diseases and Residue Safety Assessment

2018· article· en· W2867741138 on OpenAlexvenueno aff
Yueyi Song, Jianshu Chen, Xiaojun Chen, Qingxia Zhang, Li Ren, Zhiyuan Meng, Yajun Ren, Lingjun Guan, Tianle Fan, Dianjing Shen

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsnot available
FundersSix Talent Peaks Project in Jiangsu ProvinceGovernment of Jiangsu Province
KeywordsFungicideStrobilurinFludioxonilPreservativeHorticultureToxicologyAzoxystrobinChemistryBiologyFood science

Abstract

fetched live from OpenAlex

Ethofenprox, pyraclostrobine, trifloxystrobin and enestroburin are strobilurin fungicides that can effectively control fungal diseases caused by ascomycetes, zygomycetes, imperfect fungi, etc. With the purpose of guaranteeing the safe use of strobilurin fungicides in the prevention and treatment of citrus diseases, toxicity and control effects of the fungicides on citrus storage diseases and GC-MS (gas chromatography-mass spectrometry) were applied in this study to determine its residual safety. The results indicated that ethofenprox, trifloxystrobin, pyraclostrobine, and enestroburin had excellent inhibitory effects on citrus storage diseases at concentration of 200-400 ug/mL. Degradation dynamics of 4 fungicides during the storage period of citrus could be expressed as the first-order kinetics equation. The fungicides could penetrate into flesh through peels slowly. Therefore, the residue content of the fungicide on peels was higher than that in the flesh of the same citrus. After citrus fruits were treated for 90 d, the residues were lower than the maximum residue limits in all the countries, so the citrus were safe. This investigation provided the theoretical guidance and technical support for the quality evaluation of citrus products.

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)0.001
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 score0.917
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.245
Teacher spread0.237 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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