Bibliographic record
Abstract
I first meet Bob in Halifax sometime in 1967/8. I was a graduate student studying deep-sea plankton at Dalhousie University and shared a lab space with his wife Shirley Conover. At a coffee break, with our con-versation centered on canoeing the backwoods of Nova Scotia, Shirley asked me to please take her husband with us. It was a little daunting to include such an eminent zooplankton ecologist. I was, at the time, apprehensive about passing the dreaded comprehensive exams. However Bob Conover turned out to be just as, if not more, casually dressed as a graduate student with absolutely no airs about him. This encounter led to a life-long friendship. Bob was immediately at the forefront of his field with the publication of his doctorate from Yale in 1955 under the guidance of Gordon R. Riley. Gordon was also my PhD supervisor so I know that Bob developed his own thesis. Gordon firmly believed that the supervi-sor’s role was to assist, not direct, students so they would be able to succeed once they graduated. Bob’s thesis dealt with understanding the physiological and ecological underpinnings that enabled two sympatric copepods to coexist or share the coastal waters of Long Island Sound (Conover, 1956). He continued his research on the feeding physiology of copepods at the
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.589 | 0.449 |
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".