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UV protectants for <i>Candida oleophila</i> (strain O), a biocontrol agent of postharvest fruit diseases

2010· article· en· W2028724689 on OpenAlexafffund
Rachid Lahlali, B. Raffaele, M. Haïssam Jijakli

Bibliographic record

VenuePlant Pathology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsPenicillium expansumPostharvestBiologyAscorbic acidIn vivoRiboflavinStrain (injury)MicrobiologyFood scienceBotrytis cinereaHorticultureBiotechnology

Abstract

fetched live from OpenAlex

This study investigated the influence of UV‐B radiation (280–320 nm) on survival of Candida oleophila strain O, an antagonist yeast that prevents postharvest diseases caused by Botrytis cinerea and Penicillium expansum on apple and pear fruits. Lethal doses (LD 50 and LD 90 ) were, respectively, 0·89 and 1·45 Kj m −2 for in vitro exposure and 3·06 and 5·5 Kj m −2 for in vivo exposure. A screening test of UV‐B protectants for strain O was also evaluated under in vitro and in vivo conditions. The in vitro results showed that sodium ascorbate (0·1% and 0·01%), riboflavin (0·1%) and uric acid (0·1% and 0·01%) were the most effective and most suitable protectants. However, only riboflavin (0·1%) and uric acid (0·1%) were effective under in vivo conditions. The efficacy obtained with strain O against P. expansum , when subjected to UV‐B radiation, was 75·0% and 49·2% for pathogen concentrations of 10 5 and 10 6 spores mL −1 , respectively. Adding riboflavin to strain O gave a similar efficacy (64·2%). Applying strain O together with uric acid (0·1%) was less active (47·7%). Nonetheless, its efficacy when applied with the antioxidants sodium ascorbate (71·1%) or ascorbic acid (82·5%) was the greatest. Riboflavin and uric acid were the most cost‐effective protectants, and could be included in the final formulation of strain O when applied preharvest.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

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

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 head0.224
Teacher spread0.216 · 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 designBench or experimental
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

Citations28
Published2010
Admission routes2
Has abstractyes

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