Evidence map›Paper›PMID 42429911›Full record

ArticleTropical animal health and production2026

Evaluating machine learning algorithms for accurate prediction of yearling weight in Magra sheep.

Nitesh Jat, Virendra Kumar, Ashish Chopra, R A Lehga, Urmila Pannu

Abstract read
PubMed Publisher
In one paragraph

Article in Tropical animal health and production, 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

5 authors.

Nitesh JatAnimal Genetics and Breeding Department, College of Veterinary and Animal Science, Rajasthan University of Veterinary and Animal Sciences, Bikaner, 334001, India. nitesh.bana78@gmail.com.
Virendra KumarAnimal Genetics and Breeding Department, College of Veterinary and Animal Science, Rajasthan University of Veterinary and Animal Sciences, Bikaner, 334001, India.
Ashish ChopraAnimal Genetics and Breeding Department, College of Veterinary and Animal Science, Rajasthan University of Veterinary and Animal Sciences, Bikaner, 334001, India.
R A LehgaAnimal Genetics and Breeding Department, College of Veterinary and Animal Science, Rajasthan University of Veterinary and Animal Sciences, Bikaner, 334001, India.
Urmila PannuAnimal Genetics and Breeding Department, College of Veterinary and Animal Science, Rajasthan University of Veterinary and Animal Sciences, Bikaner, 334001, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study was designed to predict the Twelve-month body weight from live body weights at different age of Magra sheep, reared on Arid Region Campus (ICAR-Central Sheep and Wool Research Institute), Bikaner, Rajasthan using machine learning algorithms. Twelve-month body weight (MWT12) was predicted using several predictors including pedigree records, sex, period, season, dam age at lambing, dam weight at lambing, and early body weight traits such as birth weight (BWT), weaning weight (MWT3), and six-month weight (MWT6). Data of 5,470 Magra sheep born from 1998 to 2023 were used. Comparative analysis was conducted by using Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Bayesian Regression (BR), Support Vector Machine (SVM) and Gradient Boosting Machine (GBM). The algorithms were compared based on goodness of fit criteria including coefficient of determination (R

Indexed as

Body WeightMachine LearningSheep, DomesticAnimalsBayes TheoremBirth WeightBoosting Machine Learning AlgorithmsFemaleLinear ModelsMaleNeural Networks, ComputerPrediction AlgorithmsPredictive Learning ModelsSheepSupport Vector MachineGradient boosting machineMachine learningMagra sheepPrediction

Identifiers

PMID42429911

What OpenQuestion holds

Textmetadata
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.