Postharvest Disease Management of ‘Akizuki’ Pear in China: Identification of Fungal Pathogens and Control Efficacy of Chlorine Dioxide
Bibliographic record
Abstract
The ‘Akizuki’ pear has become increasingly popular in China in recent years. However, the ‘Akizuki’ pear often suffers from severe rot diseases during the postharvest storage period. Those during storage have not been thoroughly elucidated In this study, fungal pathogens causing postharvest decay of ‘Akizuki’ pear were identified through multi-gene phylogenetic analysis, followed by assessment of the antifungal efficacy of chlorine dioxide (ClO2) at varying concentrations. A total of 18 strains were isolated and identified as pathogens by Koch postulates. The isolated pathogens were taxonomically identified by combining morphological characterization of hyphae/spores with multi-gene phylogeny (ITS, β-tub, tef1). The results revealed that isolates A1-A11 were identified as Alternaria alternata, D1-D3 as Diaporthe eres, P1 as Penicillium citrinum, and P2-P4 as Penicillium expansum. The strain with the strongest pathogenicity in each genus was selected as the representative strain for subsequent control experiments. ClO2 significantly inhibited the development of the D. eres, A. alternata, and P. expansum by suppressing mycelial growth and disrupting cell membrane structure of pathogens, in which the EC50 values were 35.56 mg/L, 24.71 mg/L, and 41.98 mg/L, respectively, showing comparable antifungal activity to conventional fungicides. This has clarified the occurrence and control of postharvest decay diseases of ‘Akizuki’ pear fruit and provided more options for the practical applications in postharvest disease control of pear fruits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".