E. Camacho-Pérez

8.3k total citations
24 papers, 96 citations indexed

About

E. Camacho-Pérez is a scholar working on Animal Science and Zoology, Genetics and Small Animals. According to data from OpenAlex, E. Camacho-Pérez has authored 24 papers receiving a total of 96 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Animal Science and Zoology, 14 papers in Genetics and 9 papers in Small Animals. Recurrent topics in E. Camacho-Pérez's work include Effects of Environmental Stressors on Livestock (17 papers), Genetic and phenotypic traits in livestock (14 papers) and Animal Behavior and Welfare Studies (9 papers). E. Camacho-Pérez is often cited by papers focused on Effects of Environmental Stressors on Livestock (17 papers), Genetic and phenotypic traits in livestock (14 papers) and Animal Behavior and Welfare Studies (9 papers). E. Camacho-Pérez collaborates with scholars based in Mexico, Brazil and Türkiye. E. Camacho-Pérez's co-authors include Alfonso Juventino Chay‐Canul, Ricardo García‐Herrera, Antônio Leandro Chaves Gurgel, Omar Rodríguez-Abreo, Einar Vargas‐Bello‐Pérez, Frédèric Thalasso, Juvenal Rodríguez‐Reséndiz, Luís Carlos Vinhas Ítavo, Gelson dos Santos Difante and Thobela Louis Tyasi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Sensors and Journal of Biotechnology.

In The Last Decade

E. Camacho-Pérez

21 papers receiving 94 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
E. Camacho-Pérez Mexico 6 53 42 25 15 11 24 96
E. C. Pimenta Filho Brazil 5 51 1.0× 45 1.1× 49 2.0× 19 1.3× 7 0.6× 22 101
Rodrigo de la Barra Chile 8 63 1.2× 83 2.0× 59 2.4× 4 0.3× 14 1.3× 21 126
Radomir Savić Serbia 8 68 1.3× 74 1.8× 43 1.7× 25 1.7× 19 1.7× 37 161
Sharif Uddin Khan Pakistan 7 59 1.1× 35 0.8× 13 0.5× 3 0.2× 14 1.3× 17 308
A. Barker United States 3 24 0.5× 80 1.9× 137 5.5× 17 1.1× 12 1.1× 5 155
Omar Cristobal-Carballo New Zealand 4 24 0.5× 24 0.6× 61 2.4× 11 0.7× 4 0.4× 9 84
G. Usai Italy 2 14 0.3× 88 2.1× 21 0.8× 5 0.3× 13 1.2× 3 97
D.M. Taysom United States 4 26 0.5× 32 0.8× 109 4.4× 6 0.4× 6 0.5× 7 119
M. López Spain 8 109 2.1× 5 0.1× 16 0.6× 15 1.0× 12 1.1× 10 147
María Esperanza Cerón‐Cucchi Argentina 6 11 0.2× 7 0.2× 36 1.4× 9 0.6× 14 1.3× 15 86

Countries citing papers authored by E. Camacho-Pérez

Since Specialization
Citations

This map shows the geographic impact of E. Camacho-Pérez's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by E. Camacho-Pérez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites E. Camacho-Pérez more than expected).

Fields of papers citing papers by E. Camacho-Pérez

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by E. Camacho-Pérez. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by E. Camacho-Pérez. The network helps show where E. Camacho-Pérez may publish in the future.

Co-authorship network of co-authors of E. Camacho-Pérez

This figure shows the co-authorship network connecting the top 25 collaborators of E. Camacho-Pérez. A scholar is included among the top collaborators of E. Camacho-Pérez based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with E. Camacho-Pérez. E. Camacho-Pérez is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Tırınk, Cem, M. A. Segura Delgado, Armando Gómez-Vázquez, et al.. (2025). Classification of FAMACHA© Scores with Support Vector Machine Algorithm from Body Condition Score and Hematological Parameters in Pelibuey Sheep. Animals. 15(5). 737–737. 1 indexed citations
2.
3.
Camacho-Pérez, E., Cem Tırınk, Ricardo García‐Herrera, et al.. (2025). Predicting dry matter intake in Pelibuey sheep using machine learning methods. Heliyon. 11(2). e41913–e41913.
4.
Castillo-Atoche, Alejandro, et al.. (2024). Geo-Sensing-Based Analysis of Urban Heat Island in the Metropolitan Area of Merida, Mexico. Sensors. 24(19). 6289–6289. 3 indexed citations
5.
6.
Chay‐Canul, Alfonso Juventino, et al.. (2024). Neural Network-Based Body Weight Prediction in Pelibuey Sheep through Biometric Measurements. SHILAP Revista de lepidopterología. 12(5). 59–59. 3 indexed citations
7.
Camacho-Pérez, E., et al.. (2024). MediaPipe Frame and Convolutional Neural Networks-Based Fingerspelling Detection in Mexican Sign Language. SHILAP Revista de lepidopterología. 12(8). 124–124. 5 indexed citations
8.
Tırınk, Cem, et al.. (2023). Prediction of carcass tissues composition using the neck and shoulder traits in hair lambs with multiresponse multivariate adaptive regression splines. Small Ruminant Research. 227. 107090–107090. 1 indexed citations
9.
Andueza‐Noh, Rubén H., et al.. (2023). Morphological differentiation and seed quality of Lima bean (Phaseolus lunatus L.). Genetic Resources and Crop Evolution. 71(1). 69–81. 4 indexed citations
10.
Camacho-Pérez, E., et al.. (2023). EQUATIONS FOR BODY WEIGHT ADJUSTMENTS IN BLACK BELLY EWE LAMBS. Tropical and Subtropical Agroecosystems. 27(1).
11.
Camacho-Pérez, E., et al.. (2023). Utilidad de las ecuaciones de ajuste de peso vivo en ovinos de pelo. 16(1). 1 indexed citations
12.
García‐Herrera, Ricardo, E. Camacho-Pérez, Thobela Louis Tyasi, et al.. (2023). Predicting live weight using body volume formula in lactating water buffalo. Journal of Dairy Research. 90(2). 138–141. 5 indexed citations
13.
Camacho-Pérez, E., et al.. (2023). Predicting carcass tissue composition in Blackbelly sheep using ultrasound measurements and machine learning methods. Tropical Animal Health and Production. 55(5). 300–300. 7 indexed citations
14.
Camacho-Pérez, E., Juan Carlos Ángeles-Hernández, Antônio Leandro Chaves Gurgel, et al.. (2022). Models to predict live weight from heart girth in crossbred beef heifers. Tropical Animal Health and Production. 54(5). 275–275. 8 indexed citations
15.
García‐Herrera, Ricardo, et al.. (2022). Prediction of live weight in growing hair sheep using the body volume formula. Arquivo Brasileiro de Medicina Veterinária e Zootecnia. 74(3). 483–489. 7 indexed citations
16.
Canúl-Solís, Jorge Rodolfo, E. Camacho-Pérez, Antônio Leandro Chaves Gurgel, et al.. (2022). Prediction of live weight in beef heifers using a body volume formula. Arquivo Brasileiro de Medicina Veterinária e Zootecnia. 74(6). 1127–1133. 4 indexed citations
17.
Chay‐Canul, Alfonso Juventino, et al.. (2022). A novel model for estimating the body weight of Pelibuey sheep through Gray Wolf Optimizer algorithm. Journal of Applied Animal Research. 50(1). 635–642. 4 indexed citations
18.
García‐Herrera, Ricardo, et al.. (2022). Comparison of mathematical models to estimate live weight through heart girth in growing Pelibuey sheep. Revista Colombiana de Ciencias Pecuarias. 36(2). 89–97. 4 indexed citations
19.
García‐Herrera, Ricardo, et al.. (2021). RELATIONSHIP BETWEEN BODY VOLUME AND BODY WEIGHT IN PELIBUEY EWES. Tropical and Subtropical Agroecosystems. 24(3). 13 indexed citations
20.
Camacho-Pérez, E., et al.. (2014). Characterization of oxygen transfer in a 24-well microbioreactor system and potential respirometric applications. Journal of Biotechnology. 186. 58–65. 12 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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