CURIOSITY, BIFURCATION AND CHAOS: A TRIBUTE TO BILL RICKER’S INQUIRING MIND
Bibliographic record
Abstract
Bill Ricker retired from his position as scientist at the Pacific Biological Station in Nanaimo, British Columbia, three years before I began working there. Because he retained an office and used it frequently, I didn’t realize at first that he was, in fact, retired. Although I knew of his reputation as an accomplished scientist, it took me many years to appreciate the scope of his achievements. I began working in fisheries as a naïve mathematician, with much to learn about biology and the broader world of scientific inquiry. Bill brought a compelling curiosity to any subject that interested him. For example, he introduced me to the controversies about crop circles with the investigative article by Nickell and Fischer (1992). As a mathematician, I had the opportunity to participate in some of Bill’s mathematical recreations. He would occasionally pass me articles of interest, such as Stewart’s (1995) discussion of flower structures. What algorithm could possibly explain the beautifully tight packing of seeds on the head of a sunflower? A possible answer relates to: • the Fibonacci sequence 1, 1, 2, 3, 5, 8, 13, 21, 34,…, in which each number is the sum of the previous two, 5 −1 ϕ 1 • the golden number ϕ = defined by the equal ratios = , and 2
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".