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Trends in Calf Mortality: A Bibliometric Overview

2025· article· en· W4412364714 on OpenAlexaboutno aff
Ayşe Övgü Şen, Rabia Albayrak Delialioğlu, Yasin Altay, Akdoğan Kaan Can Tekbilek

Bibliographic record

VenueBlack Sea Journal of Agriculture · 2025
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsnot available
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

The main goal of cattle breeding is to maximize profitability through improved production and reproductive performance. In dairy farming, the target is to calve once a year, with the expectation that these calves survive. Calf mortality is not just a problem; it is a major threat to the sustainability of operations and the welfare of animals. It is a key indicator of the overall condition of cattle farms, reflecting their economic, health, and welfare status. Calf mortality refers to the losses occurring from birth up to six months of age, directly affecting the profitability and well-being of the herd. While it is recommended that calf losses do not exceed 5%, reported perinatal mortality rates in cows and calves range from 2% to 20%, with most countries recording rates between 5% and 8%. This study conducted a bibliometric analysis based on data retrieved from the Web of Science (WoS) database to evaluate the scientific literature on calf mortality. The analysis was based on data retrieved from the Web of Science (WoS) database, where a search was performed on titles, keywords, and abstracts. A total of 2359 publications from the period 1945–2025 were identified and analyzed using the bibliometrix package in R software, focusing on citation networks and bibliographic linkages. The findings indicate a growing academic interest in calf mortality recently. Regarding publication types, research articles constituted the majority (2108), followed by conference papers (27), reviews (124), editorial notes (11), book chapters (6) and others (83). While the number of countries conducting scientific research on calf mortality is quite high, the USA (1358), Canada (583), and UK (315) are leading nations in terms of both domestic studies and collaborations with other countries. These results highlight the increasing academic interest in calf mortality and the expanding range of research contributions in this area and may provide practical information for cattle breeders in the field.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.756
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.2440.293
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.087
GPT teacher head0.398
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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