Early career researchers: an interview with Graham Scott
Bibliographic record
Abstract
Graham Scott is an Assistant Professor at McMaster University, Canada, where he studies the integrative biology of how animals cope in challenging environments. He received his Bachelor's degree in biology before completing a Master's degree with Trish Schulte and then a PhD in 2009 with Bill Milsom at the University of British Columbia, Canada. He moved on to continue his postdoc training with Ian Johnston at the University of St Andrews, UK. Scott received the Animal Section Presidents' Medal from the Society for Experimental Biology in 2012, he was an author on the Journal of Zoology Paper of the Year in 2015 and he was awarded the Robert G. Boutilier New Investigator Award by the Canadian Society of Zoologists in 2017.
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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.036 | 0.017 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.013 | 0.038 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".