ArticleVeterinary medicine and science2025
Body Weight Estimation in Holstein × Zebu Crossbred Heifers: Comparative Analysis of XGBoost and LightGBM Algorithms.
Article in Veterinary medicine and science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- Evaluation of the performance of the Light Gradient Boosting Machine (LightGBM) algorithm in predicting live weight in different goat breeds.BMC veterinary research · 2026Article
- Speeding Up the Discovery of Optimal Feature Combinations for Omics Data Based on Pseudo-Kernel Function.Research square · 2026Article
- Application of Machine Learning Algorithms in Estimating Live Weight of Yucatecan Criollo Pigs Through Biometric Measurements.Animals : an open access journal from MDPI · 2026Article
- Non-invasive Prediction of Lung Cancer Histological Differentiation via Radiomics and Multi-binary Classification ModelsCurrent medical imaging · 2026Article
- Recent feeding history drives prediction of daily feed intake in lactating sows.Translational animal science · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study evaluates the effectiveness of XGBoost and LightGBM algorithms for estimating the live weight of Holstein×Zebu crossbred heifers. The study compares the performance of both algorithms using a wide range of biometric measurements and tests various hyperparameter settings. The research results show that the XGBoost algorithm provides almost perfect agreement with an 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.