In the field: an interview with Chris Wood
Bibliographic record
Abstract
Chris Wood is an Adjunct Professor at the University of British Columbia (UBC) and an Emeritus Distinguished Professor at McMaster University, Canada, investigating physiology and aquatic toxicology. After completing his BSc and MSc in Zoology with Dave Randall at UBC and a PhD with Graham Shelton at University of East Anglia, UK, Wood worked as a post-doc with Brian McMahon at the University of Calgary before joining the Biology Department, McMaster University, Canada. Wood talks about his experiences on the legendary R/V Alpha Helix expedition to the Amazon in 1976, his development of the Tusker Chamber in Kenya and more recent adventures at the Bamfield Marine Sciences Center on Vancouver Island, Canada.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.014 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.022 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".