Diagnosis of Induced Resistance State in Tomato Using Artificial Neural Network Models Based on Supervised Self-Organizing Maps and Fluorescence Kinetics.
Article in Sensors (Basel, Switzerland), 2022. 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
0.5field-weighted citation impact, top 43% of its field
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, 4 citations in OpenAlex.
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
11 authors at 3 institutions in 1 country.
Xanthoula Eirini PantaziLaboratory of Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.ORCID 0000-0002-8039-1077
Anastasia L LagopodiLaboratory of Plant Pathology, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.ORCID 0000-0003-3225-8033
Afroditi Alexandra TamouridouLaboratory of Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Nathalie Nephelie KamouLaboratory of Plant Pathology, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Ioannis GiannakisLaboratory of Plant Pathology, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Georgios LagiotisInstitute of Applied Biosciences, Centre for Research and Technology Hellas, Thermi, 57001 Thessaloniki, Greece.
Evangelia StavridouInstitute of Applied Biosciences, Centre for Research and Technology Hellas, Thermi, 57001 Thessaloniki, Greece.
Panagiotis MadesisInstitute of Applied Biosciences, Centre for Research and Technology Hellas, Thermi, 57001 Thessaloniki, Greece.ORCID 0000-0003-1447-3514
Georgios TziotziosLaboratory of Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Konstantinos DolaptsisLaboratory of Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Dimitrios MoshouLaboratory of Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.ORCID 0000-0001-7270-5307
Aristotle University of Thessaloniki · GRCentre for Research and Technology Hellas · GRUniversity of Thessaly · GR
Funding
No grant is acknowledged in the PubMed record.
6 · The paper itself
Abstract
The aim of this study was to develop three supervised self-organizing map (SOM) models for the automatic recognition of a systemic resistance state in plants after application of a resistance inducer. The pathosystem
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.
Diagnosis of Induced Resistance State in Tomato Using Artificial Neural Network Models Based on Supervised Self-Organizing Maps and Fluorescence Kinetics. · full record | OpenQuestion