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Record W1997375068 · doi:10.2527/jas.2008-1369

MEMORIAL

2008· article· en· W1997375068 on OpenAlexaboutno aff
Mary E. Delany, José Federico Medrano

Bibliographic record

VenueJournal of Animal Science · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsLivestockAgricultureAnimal husbandryLibrary scienceAnimal agricultureAnimal productionPolitical scienceManagementSociologyAgricultural scienceGeographyAnimal scienceBiologyArchaeologyEcology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.137

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.266
Teacher spread0.249 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2008
Admission routes1
Has abstractyes

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