Bernard Michael Bindon — reproductive physiologist, animal scientist, research leader
Bibliographic record
Abstract
This paper is a foreword to a series of papers commissioned on ‘the impact of science on the beef industry’, where the Beef CRC-related collaborative scientific work of Professor Bernard Michael Bindon will be reviewed. These papers will be presented in March 2006, as part of a ‘festschrift’ to recognise his wider contributions to the Australian livestock industries for over 40 years. Bindon’s career involved basic and applied research in many areas of reproductive physiology, genetics, immunology, nutrition, meat science and more recently genomics, in both sheep and cattle. Together with his collaborators, he made large contributions to animal science by improving the knowledge of mechanisms regulating reproductive functions and in elucidating the physiology and genetics of high fecundity livestock. His collaborative studies with many colleagues of the reproductive biology and genetics of the Booroola Merino were amongst the most extensive ever conducted on domestic livestock. He was instrumental in the development of immunological techniques to control ovulation rate and in examining the application of these and other techniques to increase beef cattle reproductive output. This paper tracks his investigations and achievements both within Australia and internationally. In the later stages of his career he was the major influence in attracting a large investment in Cooperative Research Centres for the Australian cattle industry, in which he directed a multi-disciplinary approach to investigate, develop and disseminate science and technology to improve commercial cattle productivity.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".