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Record W4386705456 · doi:10.1094/pdis-07-23-1305-re

Cluster Zone Leaf Removal Reduces the Rate of Anthracnose (<i>Elsinöe ampelina</i>) Progress and Facilitates Its Control

2023· article· en· W4386705456 on OpenAlexafffund
Odile Carisse, Caroline Provost

Bibliographic record

VenuePlant Disease · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsSonaca (Canada)Agriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsBiologyCluster (spacecraft)HorticultureBotany

Abstract

fetched live from OpenAlex

Anthracnose caused by Elsinöe ampelina is an economically important disease that affects certain hardy and semihardy grapevine cultivars. The control of this disease requires repeated application of fungicides, which has financial and environmental consequences. In this study, leaf removal in the cluster area was studied with a view to facilitating integrated anthracnose management. First, the effect of leaf removal timing (BBCH stage 53 or 71) and intensity (one or both sides of rows) on the progression of anthracnose and on the microclimate was studied in plots planted with Vidal blanc (Vitis vinifera) at two sites in both 2020 and 2021. Overall, at both sites and in both years, anthracnose on leaves was more severe in plots without cluster zone leaf removal. Regardless of the timing of leaf removal, anthracnose severity on leaves and incidence of infected berries at harvest were significantly lower in plots where leaves had been removed on both sides of the rows compared with plots where leaves were removed on one side only. Second, anthracnose management programs with leaf removal, with or without disease risk estimation, were evaluated. All anthracnose management programs including leaf removal in the cluster zone reduced anthracnose development compared with the standard program without leaf removal. Overall mean leaf anthracnose severity, severity at harvest, and anthracnose incidence on clusters at harvest were lower in plots with leaf removal than in the standard program, but the differences between the two treatments were not significant (P &gt; 0.05). More fungicide applications were made in plots managed using the standard programs, specifically 13 applications, compared with plots managed based on assessing the weather-related risk of anthracnose, with 9 and 10 applications made at sites 1 and 2 for the risk-based program, respectively, and 5 and 7 applications made at sites 1 and 2, respectively, when microclimate within the cluster zone was considered. The results of this study clearly show the important role that leaf removal can play in managing grape anthracnose.

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 score0.937
Threshold uncertainty score0.235

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 head0.032
GPT teacher head0.269
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

Citations4
Published2023
Admission routes2
Has abstractyes

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