Analysis on Outputs of Dairy Cows Research Papers in the World Based on the Web of Science
Bibliographic record
Abstract
This study was designed to get an overview of the globaldairy cowresearch by analysis outputs ofdairy cowresearch papers during 2004—2013 based on the web of science. Based on the topic words dairy cow recorded by web of science database, this paper analyzes the distribution of subjects, countries/regions, research institutes, key journals and top 20 authors published articles and top 10 cited articles. USA is the leading country in this respect. The core journal aboutdairy cowresearch isJournal of Dairy Science. The largest number of papers published is the United States, followed by Canada and Germany. The Agriculture and Agriculture Food of Canada, INRA(France) and Aarhus University are the top 3 institutions that published the largest numbers of articles about the dairy cow. The Chinese Academy of Agricultural Sciences and China Agricultural University are top 2 in papers published in the domestic. The focuses on dairy cowresearch are agricultural, veterinary science and food science and technology. The most citation paper is from the University of Guelph in Canada. Results suggest that the USA should be the target indairy cowresearch and focus on veterinary science and food science and technology.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.099 | 0.174 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".