MétaCan
Menu
Back to cohort
Record W2969358557 · doi:10.1094/pdis-11-18-2046-re

Distribution of the Infection Time of Ascospores of <i>Venturia inaequalis</i>

2019· article· en· W2969358557 on OpenAlexafffund
Vincent Philion, Valentin Joubert, M. Trapman, Anne-Grete Roer Hjelkrem, Arne Stensvand

Bibliographic record

VenuePlant Disease · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsInstitut de Recherche et de Développement en Agroenvironnement
FundersNorges ForskningsrådMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
KeywordsVenturia inaequalisBiologySporeAscosporeLeaf wetnessInoculationHorticultureIncubation periodFungicideBotanyPopulationApple scabRosaceaeIncubation

Abstract

fetched live from OpenAlex

In many areas where spring is wet, fungicides are applied in relation to rain events that trigger ejection of ascospores of Venturia inaequalis, which cause primary infections of apple scab. Past studies established the rate of ejection during rain in relation to light and temperature, and determined the wetting time required for infection. Simulation software uses this information to calculate risk and help time sprays accordingly. However, the distribution of the infection time required by a population of spores landed on leaves was never studied, and assumptions were used. To estimate this, we inoculated ascospores of V. inaequalis on potted trees at different temperatures for specific wetting times. Lesions were enumerated after incubation. Lesions increased with wetness time and leveled off once the slowest spores infected the host, closely matching the monomolecular model. Wetness hours were best adjusted for temperature using the Yin equation. The minimum infection time on the youngest leaves was about 5 h, matching results from previous studies, whereas half the lesions appeared after 7 h of infection. Infection times for leaves with ontogenic resistance were longer. Our results improve current software estimates and may improve spraying decisions.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.203

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 head0.004
GPT teacher head0.196
Teacher spread0.191 · 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

Citations13
Published2019
Admission routes2
Has abstractyes

Explore more

Same venuePlant DiseaseSame topicPlant Pathogens and Fungal DiseasesFrench-language works237,207