Evidence map›Paper›PMID 36771717›Full record

ArticlePlants (Basel, Switzerland)2023

Big Data and Machine Learning to Improve European Grapevine Moth (

Joaquín Balduque-Gil, Francisco J Lacueva-Pérez, Gorka Labata-Lezaun, Rafael Del-Hoyo-Alonso, Sergio Ilarri, Eva Sánchez-Hernández, Pablo Martín-Ramos, Juan J Barriuso-Vargas

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plants (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Joaquín Balduque-GilDepartment of Agricultural Sciences and Natural Environment, AgriFood Institute of Aragon (IA2), University of Zaragoza, Avenida Miguel Servet 177, 50013 Zaragoza, Spain.ORCID 0000-0001-5136-8639
Francisco J Lacueva-PérezDepartment of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain.ORCID 0000-0003-0998-2939
Gorka Labata-LezaunDepartment of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain.
Rafael Del-Hoyo-AlonsoDepartment of Big Data and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain.
Sergio IlarriDepartamento de Informática e Ingeniería de Sistemas, Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza, María de Luna 1, 50018 Zaragoza, Spain.ORCID 0000-0002-7073-219X
Eva Sánchez-HernándezDepartment of Agricultural and Forestry Engineering, ETSIIAA, University of Valladolid, Avenida de Madrid 44, 34004 Palencia, Spain.ORCID 0000-0001-8983-3954
Pablo Martín-RamosDepartment of Agricultural and Forestry Engineering, ETSIIAA, University of Valladolid, Avenida de Madrid 44, 34004 Palencia, Spain.ORCID 0000-0003-2713-2786
Juan J Barriuso-VargasDepartment of Agricultural Sciences and Natural Environment, AgriFood Institute of Aragon (IA2), University of Zaragoza, Avenida Miguel Servet 177, 50013 Zaragoza, Spain.

Funding

European Union's Connecting Europe Facility (CEF) INEA/CEF/ICT/A2018/1837816 GRAPEVINE project (hiGh peRformAnce comPuting sEr- 523 vices for preVentIon and coNtrol of pEsts in fruit crops)
6 · The paper itself

Abstract

Machine Learning (ML) techniques can be used to convert Big Data into valuable information for agri-environmental applications, such as predictive pest modeling.

Indexed as

data-driven modelsintegrated pest managementIoTLobesia botranamachine learningpest monitoringpredictive modelsweather data

Identifiers

PMID36771717
PMCPMC9921845

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.