Bibliographic record
Abstract
Abstract This paper discusses the people and events that influenced my career and the lessons I learnt along the way. In this essay I attempt to pass these lessons on to others. Born and growing up in Cape Town, from my earliest recollections at the age of four I have wanted to be associated with fish and the sea. My first mentor was the Professor of Zoology at the University of Cape Town, who gave me the opportunity to go to sea on the University research ship, and later to join the International Indian Ocean Expedition where I met others who influenced me. Then a postdoctoral fellowship in Nova Scotia led me to meet Ken Mann, a SCOR working group, and a series of wonderful colleagues and friends. The contribution of each of these to my development is discussed, along with the lessons learned. The friends I have made on my journey in marine science and my academic family of former students have enriched my life enormously and if I had my life again, I would have it no other way.
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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".