A Tribute to Dr. Ronald Hardy for his Contribution to Aquaculture Nutrition
Bibliographic record
Abstract
Ronald William Hardy was born in 1947 in Vancouver, Canada. He comes from a very academically able Canadian/Scottish family. His grandparents came from Scotland and moved to Vancouver in 1909. In his father´s family there were doctors, nurses and also missionaries in Canada near Alaska. Ron´s father worked in agriculture communities around Seattle (an expert in poultry science), and used to take his 6 years old son salmon fishing, that was Ron´s first connection with salmon and trout. His mother came from a family of Scottish farmers, and then scientists; she was a Microbiologist and worked on tuberculosis. Ron took pre-medicine curriculum for 4 years, receiving his BS in Zoology in 1969 at the University of Washington. He took many jobs, including on farms and railroads, to pay for his college education. In 1970 he married Elizabeth the future mother of his daughters (Anna and Clare). Then in 1973, he obtained a M.S. in Animal Sciences/Nutrition at Washington State University; his thesis subject was “Studies on factors in rye which cause growth depression in chicks”. One day at the University Library he found the book of Dr. Halver on fish nutrition and, discovering the important gap in this area with respect to poultry, porcine and bovine nutrition, realized the huge potential of this new activity. Halver´s book was his second inspiration… It was at this time that he commenced his life‟s work on the nutrition of fish, graduating with a PhD in Fisheries at the University of Washington, Seattle (1978). Hardy‟s PhD dissertation subject was “Effects of dietary protein and pyridoxine levels on growth and disease resistance of chinook salmon”, having as mentors Dr. Halver (biochemistry and nutrition) and Dr. Brannon (salmon biology).
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.081 | 0.048 |
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".