Bibliographic record
Abstract
Abstract An invitation to write a thought piece is an opportunity to reflect on one's early career, when events and choices shaped what we became. Because I teach at a university, I wrote a narrative for those thinking of becoming an ocean scientist, and are now encountering the research literature. It is a chronicle of episodes, each a discovery or surprising event and what I learned. It moves from early memories of the natural world to first attempts at science. A near-fatal surprise led to a course in environmental policy, to a commitment to public good science, and to learning benthic ecology in Woods Hole. Failure at publishing an original finding from a thesis spurred me to self-directed practice in writing scientific prose. Skill at running statistical analyses resulted in a post-doc on a topic where I had no other expertise. Publication success led to a second post-doc and a faculty position in Canada, where I funded three decades of student-oriented research, taught statistics, and wrote a book on ecological scaling from the point of view of an oceanographer. Teaching statistics came with surprises, which I list as further food for thought.
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 | 0.029 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.025 | 0.020 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".