Bibliographic record
Abstract
Eric Bradford's interests in animal agriculture began while growing up on a small, mixed farm in Quebec Province, Canada. He was a graduate of Macdonald College of McGill University with a BS degree with honors in agriculture (1951). He was awarded his MS (1952) and PhD (1956) degrees with a joint major in genetics and animal husbandry from the University of Wisconsin. He joined McGill University as assistant professor in 1955, but moved to the Animal Husbandry department (later named the Department of Animal Science) at the University of California-Davis in 1957, rising through the ranks to professor in 1969. His career included sabbaticals at Cornell University, the Agricultural Research Council (Edinburgh, Scotland), and Winrock International, a global nonprofit organization that addresses rural development and sustainable resource management through education and empowerment programs. Bradford taught courses in animal breeding and genetics, beef cattle and sheep production, animal growth, and also general animal science, educating numerous undergraduates over the years. His research spanned reproduction and growth in livestock and laboratory (mice) animals; sheep breeding; animal genetic resources conservation; and international agriculture. Over the course of his career, he published more than 150 journal articles and book chapters, with an additional 60 general publications. He served as major professor for 11 PhD and 14 MS students from 12 countries, and he remarked that these students were a major stimulus to his interest in international agriculture.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.582 | 0.269 |
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".