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Record W2899007393 · doi:10.1094/phyto-12-17-0412-r

Timing of Grape Downy Mildew Onset in Bordeaux Vineyards

2018· article· en· W2899007393 on OpenAlexaff
Mathilde Chen, François Brun, M. Raynal, David Makowski

Bibliographic record

VenuePhytopathology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsInstitute for Work & Health
FundersAgence Nationale de la RechercheDanish Cancer Society Research Center
KeywordsDowny mildewFungicideVineBiologyHorticultureGrowing seasonMildewAgronomy

Abstract

fetched live from OpenAlex

Grapevine downy mildew (GDM) is a severe disease of grapevines. Because of the lack of reliable information about the dates of GDM symptom onset, many vine growers begin fungicide treatments early in the season. We evaluate the extent to which such preventive treatments are justified. Observational data for 266 untreated sites for the years between 2010 and 2017 were used to estimate the timing of GDM onset on vines and bunches of grapes in South West France (Bordeaux region) through survival analyses. The onset of GDM was not apparent on vines and bunches before early to mid-May, and the rate of GDM symptom appearance was highly variable across years. Depending on the year, 50% of the plots displayed symptoms between mid-May and late June for vines. For several years, our statistical analysis revealed that the proportion of plots with no symptoms was high in early August on vines (27.5 and 43.7% in 2013 and 2016) and on bunches (between 23 and 79% in 2011, 2013, and 2016). We found a significant effect of the amount of rainfall in spring on the date of symptom appearance. These results indicate that preventive fungicide application is unjustified in many vineyards, and that regional disease surveys should be used to adjust fungicide treatment dates according to local characteristics, in particular according to rainfall conditions in spring.

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 score0.883
Threshold uncertainty score0.761

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)0.0010.000

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 head0.045
GPT teacher head0.311
Teacher spread0.267 · 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
Published2018
Admission routes1
Has abstractyes

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