Evidence map›Paper›PMID 33424873›Full record

ArticleFrontiers in plant science2020

Modeling and Optimizing Culture Medium Mineral Composition for

Radhia Hameg, Tomás A Arteta, Mariana Landin, Pedro P Gallego, M Esther Barreal

Open access · goldAbstract read
In one paragraph

Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
2.7field-weighted citation impact, top 9% 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

20 citing papers in PubMed, 49 citations in OpenAlex.

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  8. Embryo Rescue in Plant Breeding.Plants (Basel, Switzerland) · 2023
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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 at 2 institutions in 1 country.

Radhia HamegApplied Plant & Soil Biology, Department of Plant Biology and Soil Sciences, Faculty of Biology, University of Vigo, Vigo, Spain.
Tomás A ArtetaApplied Plant & Soil Biology, Department of Plant Biology and Soil Sciences, Faculty of Biology, University of Vigo, Vigo, Spain.
Mariana LandinDepartamento de Farmacología, Farmacia y Tecnología Farmacéutica, R+D Pharma Group (GI-1645), Facultad de Farmacia y Agrupación Estratégica en Materiales (AeMat), Universidade de Santiago de Compostela, Santiago, Spain.
Pedro P GallegoApplied Plant & Soil Biology, Department of Plant Biology and Soil Sciences, Faculty of Biology, University of Vigo, Vigo, Spain.
M Esther BarrealApplied Plant & Soil Biology, Department of Plant Biology and Soil Sciences, Faculty of Biology, University of Vigo, Vigo, Spain.
Universidade de Vigo · ESUniversidade de Santiago de Compostela · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The design of plant tissue culture media remains a complicated task due to the interactions of many factors. The use of computer-based tools is still very scarce, although they have demonstrated great advantages when used in large dataset analysis. In this study, design of experiments (DOE) and three machine learning (ML) algorithms, artificial neural networks (ANNs), fuzzy logic, and genetic algorithms (GA), were combined to decipher the key minerals and predict the optimal combination of salts for hardy kiwi (

Indexed as

algorithmsartificial intelligencekiwiberrymineral nutritionmodelingphysiological disordersplant tissue culture

Identifiers

PMID33424873
PMCPMC7785940
OpenAlexW3118226979

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.