Bibliographic record
Abstract
I was fortunate to have met Terry Beveridge early during my time in graduate school, when my advisor, Francois Morel (a geochemist), recognized the importance of my having a microbiologist on my PhD advising committee. At the time, I was studying microbial precipitation of arsenic trisulfide (As_2S_3) by Desulfotomaculum auripigmentum and needed the help of a geomicrobiologist to take electron micrographs of my samples (Newman et al., 1997). What better person to ask than Terry Beveridge? Not knowing much about what I was doing, I naively went off to Guelph to spend a week working in Terry’s laboratory during the fall of 1994. Little did I know that this would be the start of one of the most inspirational scientific and personal relationships of my career. From that week forward, I turned to Terry whenever I had a question about microbiology, and looked forward to his responses: Terry had a way of making me feel like I had hit upon something profound (when in fact my questions were pretty trivial), and his answers not only were thoughtful and informed, but had the effect of getting me to think about other problems that were much more interesting.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".