Reflections of Automation in the Dairy Industry: A Bibliometric Analysis Approach on Robotic Milking Systems
Bibliographic record
Abstract
Objective: The main purpose of this study is to emphasize the importance of robotic milking systems for the livestock and food industry and to determine the scope of current studies by bibliometric analysis. Materials and Methods: As the material of this study, bibliographic data of 460 scientific studies published between 1989 and 14.10.2024, which are currently available in the Web of Science database, were used. Bibliometric analysis was used in the evaluation of these data. Results: The United States is the country with the most studies on the subject, with 65 articles. Following the United States, the United Kingdom and Canada were identified as other countries with a high number of studies. In terms of the number of scientific studies, Journal of Dairy Science stands out among journals with 104 articles. It is also the most cited journal. Scientific studies on the relevant subject have been carried out intensively in almost every continent. Trevor J DeVries is considered the most prolific author, having published 34 scientific articles on robotic milking systems. Among the most commonly used keywords, “dairy-cows” (67), “behavior” (61), “yield” (58) and “robotic milking” (50) stand out. These keywords are seen as a focal point in scientific studies due to the high relationship of robotic milking systems with animal behavior and productivity. This study aims to provide a valuable resource for academics and researchers by deepening the knowledge on robotic milking systems. Conclusion: The research conducted points to a significant potential in both academic and industrial fields, and it is anticipated that future studies will take this potential even further.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.168 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".