Bibliographic record
Abstract
I t is with much sadness that I write that Grant Gall died suddenly while on holiday with his wife Laurie.He might want it noted that it was with his 'older' wife Laurie because he loved to tease her over the two or three days difference in their birthdays.The two were inseparable over their 49 years of married life, and it is difficult to think of Grant without thinking of Laurie.He loved her tremendously as he did his children and grandchildren.Grant had a great sense of humour and loved to laugh.Once in a while, he would gasp for a halt in the storytelling because he was laughing so hard that his not insubstantial girth was in pain.That is how I will remember him.He was a man with a passion for life.If you have only seen him in a shirt and tie at meetings, stop and imagine Grant in short pants, a T-shirt, walking shoes and a Tilly sun hat, bristling for the adventure at hand.He loved to fish the Bow river, hunt prairie chickens, walk across countries (particularly Scotland), search for antique cars, listen to the blues and drink expensive scotch.Grant graduated from the ACME High School, of which he was very proud, then the University of Alberta Medical School,
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.018 | 0.047 |
| Insufficient payload (model declined to judge) | 0.034 | 0.018 |
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".