Evidence map›Paper›PMID 33218063›Full record

ArticleSensors (Basel, Switzerland)2020

Reliability Evaluation of the Data Acquisition Potential of a Low-Cost Climatic Network for Applications in Agriculture.

Sergio Trilles, Pablo Juan, Carlos Díaz-Avalos, Sara Ribeiro, Marco Painho

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. 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

5 authors.

Sergio TrillesInstitute of New Imaging Technologies, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castellón de la Plana, Spain.ORCID 0000-0002-9304-0719
Pablo JuanIMAC, Mathematics Department, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castelló de la Plana, Spain.ORCID 0000-0002-2197-7502
Carlos Díaz-AvalosDepartment of Probability and Statistics, Institute of Research in Applied Mathematics and Systems, Universidad Nacional Autónoma, 04510 Ciudad de México, Mexico.ORCID 0000-0002-1455-8676
Sara RibeiroNova Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1070-312 Lisbon, Portugal.ORCID 0000-0002-7469-7475
Marco PainhoNova Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1070-312 Lisbon, Portugal.ORCID 0000-0003-1136-3387

Funding

Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana GV/2020/035Ministerio de Ciencia e Innovación IJC2018-035017-I
6 · The paper itself

Abstract

Temperature, humidity and precipitation have a strong influence on the generation of diseases in different crops, especially in vine. In recent years, advances in different disciplines have enabled the deployment of sensor nodes on agricultural plots. These sensors are characterised by a low cost and so the reliability of the data obtained from them can be compromised, as they are built from low-confidence components. In this research, two studies were carried out to determine the reliability of the data obtained by different

Indexed as

AgricultureClimateCrops, AgriculturalMeteorologyPlant DiseasesReproducibility of ResultsSpainclimatic conditionscross-correlationhomogenisationlow-cost sensors

Identifiers

PMID33218063
PMCPMC7698867

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.