ArticleScientific reports2025
Predicting macroelement content in legumes with machine learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
Who cites it
2 citing papers in PubMed.
- ALife (Basel, Switzerland) · 2026Article
- Chemical and Functional Characterization of a Novel European Black Soybean Variety.Molecules (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to develop accurate and efficient machine learning models to predict the concentrations of phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) in 10 legume species naturally growing in the Çamlıhemşin district of Rize province, Türkiye. A comprehensive dataset of feed quality characteristics was collected, and four widely used machine learning algorithms-Multivariate Adaptive Regression Splines (MARS), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Artificial Neural Networks (ANN)-were employed to build predictive models. The performance of these models was evaluated using a range of statistical metrics, including root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R
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Registered trials
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.