Evidence map›Paper›PMID 41991618›Full record

ArticleScientific reports2026

Wheat seed germination prediction in response to temperature, water potential, and salinity using an artificial neural network.

Hedayatollah Karimzadeh Soureshjani, Mahmoud Bahador, Ayoub Ghorbani Dehkordi, Hamideh Ghaffari

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Hedayatollah Karimzadeh SoureshjaniDepartment of Agronomy, Sari Agricultural Sciences and Natural Resources University (SANRU), Sari, Iran. hkarimzadeh@sanru.ac.ir.
Mahmoud BahadorFars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran.
Ayoub Ghorbani DehkordiDepartment of Horticulture, Gorgan University of Agricultural Sciences & Natural Resources (GUASNR), Gorgan, Iran.
Hamideh GhaffariState Key Laboratory for Wheat Improvement, College of Life Sciences, Shandong Agricultural University, Taian, 271018, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multi-layer perceptron (MLP) neural networks can be used to develop accurate models for quantifying plant responses to environmental factors. This study aimed to quantify wheat seed germination in response to temperature, water potential, and salinity using an ANN. Results indicated that the MLP model could predict total germination percentage with high model accuracy, including R

Indexed as

GerminationSalinitySeedsTriticumWaterMultilayer PerceptronsTemperatureWaterAbiotic stressesArtificial intelligence modelingMulti-layer perceptronSeed germination simulation

Identifiers

PMID41991618
PMCPMC13246773

What Socratic 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 Socratic graph.