Bibliographic record
Abstract
Gordon Riley was an outstanding scientist who played a leading international role in the development of oceanography as a field of scientific study in the mid-twentieth century. His multidisciplinary approach, quantitative skills, imagination and intuition advanced our knowledge and understanding of the ocean enormously. Of his many significant scientific contributions to oceanography, he is best known for his pioneering work in developing simple numerical models for improving the understanding of the dynamics of marine ecosystems with a focus on plankton. He helped transform oceanography from a descriptive to a quantitative science. His early career was spent in the United States at the Bingham Oceanographic Laboratory of Yale University and the Woods Hole Oceanographic Institution. In 1965, at the peak of his career, he immigrated to Canada to become the director of the Institute of Oceanography at Dalhousie University. Under his leadership, the Institute evolved into the Department of Oceanography, which became an internationally recognized centre for marine research and teaching. During this period, he also played a prominent role in the development of the broader Canadian oceanographic community. He served as a wonderful example of how scientific research, teaching and a life should be carried out.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.023 |
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".