A SCIENTOMETRIC ANALYSIS OF CHRONIC WASTING DISEASE RESEARCH PRODUCTIVITY
Bibliographic record
Abstract
This study evaluates the chronic wasting disease research output from 1990–2021. We have made a scientometric analysis of contributions to peer-reviewed journals and international collaboration publications. Most productive authors, most productive institutions, and most cited research papers. The study uses 31 years (1990–2021) of publication data in chronic wasting disease research from the Web of Science international multidisciplinary bibliographical database. Chronic wasting disease research shared 1384 publications in collaboration with 53 countries and registered 37940 citations. There were 1384 research articles published in 409 journals, with 962 authors representing 1648 institutions. The most collaborative countries: The United States has the most collaboration records with 876, followed by Canada with 228 and the UK with 139. The most productive authors are "Miller MW," which has a 6549 global citation score with 100 publications, followed by "Hoover EA," which has a 3216 global citation score with 82 publications, and "Telling GC," which has a 2619 global citation score with 62 publications. The most preferred journals were "PRION," which has 1034 citations with 159 publications, followed by "PLOS One," which has 2021 citations with 79 publications, and "Journal of Virology," which has 2274 citations with 53 publications.
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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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.094 | 0.129 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".