Evidence map›Paper›PMID 39906864›Full record

ArticleHeliyon2025

Predicting dry matter intake in Pelibuey sheep using machine learning methods.

Enrique Camacho-Perez, Cem Tirink, Ricardo Garcia-Herrera, Ángel T Piñeiro-Vazquez, Fernando Casanova-Lugo, Jorge R Canul-Solis, Antonio Leandro Chaves-Gurgel, Ceyhun Yücel, Einar Vargas-Bello-Pérez, Alfonso J Chay-Canul

Abstract read
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In one paragraph

Article in Heliyon, 2025. 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

10 authors.

Enrique Camacho-PerezFacultad de Ingeniería. Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes s/n, Mérida, Yucatán, Mexico.
Cem TirinkIgdir University, Faculty of Agriculture, Department of Animal Science, TR76000, Igdir, Turkiye.
Ricardo Garcia-HerreraDivisión Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Carretera Villahermosa-Teapa, km 25, R/A. La Huasteca 2 Sección, Villahermosa, Tabasco, Mexico.
Ángel T Piñeiro-VazquezTecnológico Nacional de México, Mexico.
Fernando Casanova-LugoTecnológico Nacional de México, Mexico.
Jorge R Canul-SolisTecnológico Nacional de México, Mexico.
Antonio Leandro Chaves-GurgelFederal University of Piauí, 64900-000, Bom Jesus, Piauí, Brazil.
Ceyhun YücelDepartment of Animal Science, Faculty of Agriculture, University of Yozgat Bozok, 66900, Yozgat, Turkiye.
Einar Vargas-Bello-PérezDepartment of Animal Sciences, School of Agriculture, Policy and Development, University of Reading, P.O. Box 237, Earley Gate, Reading, RG6 6EU, UK.
Alfonso J Chay-CanulDivisión Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Carretera Villahermosa-Teapa, km 25, R/A. La Huasteca 2 Sección, Villahermosa, Tabasco, Mexico.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study determined to predict the dry matter intake (DMI) in growing male Pelibuey sheep by using 3 different machine learning methods. Individual data was obtained from 130 animals whose average body weight (ABW) was 23 ± 6 kg and the DMI was 1.04 ± 0.27 kg/d from an experiment conducted under tropical conditions. To create the database, the following data were recorded: % concentrate in the diet (CON), initial body weight (IBW, kg), final BW (FBW, kg), mean metabolic BW (MBW

Indexed as

Dry matter intakeHair sheepMachine learning

Identifiers

PMID39906864
PMCPMC11791229

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.