Bibliographic record
Abstract
Narrator: Charles Oliviero Interviewer: Interviewed by Logan Groicher Interview Date and Location: March 7, 2025, Zoom Synopsis 00:00-03:57 - Early life and reason for joining the Royal Military College of Canada 03:57-06:05 - Switching from Naval Cadet to the Armoured Corps 06:05-12:35 - Experiences at RMC, How the Military College worked 12:35-19:26 - Changes in RMC from his time to present day, Commandant, Commandant versus principal 19:26-25:44 - First regiment. Regimental history, Deployments served with them 25:44-31:36 - Roles served in Western Germany, German war college, Canadian Forces Europe, Why Canadian brigade was Valued 31:36-35:39 - Where he was stationed, life in Germany 35:39-40:25 - Equipment used in Germany, getting transported to Germany, Lahr staging base in Germany 40:25-44:25 - Time posted at Lahr, layout of the Lahr, touch and goes 44:25-48:33 - Working with foreign Militaries, STANAGS, Americans, radios 48:33-1:01:13 - Exercises, FALLEX, Anecdote on Exercise, how effective exercises were, Interactions with German civilians 1:01:13-1:07:04 - notions on equipment, Story about tank firing range, final thoughts on time in Germany 1:07:04-1:12:14 - Similarities and differences between Germany and Cyprus, thoughts on tank warfare 1:12:14- - Final thoughts, Ukrainian war, Importance of NATO, evolution of warfare, Roman quotes
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".