The 1999 Vimy Award: Acceptance Address by Ltd. General Charles Belzille
Bibliographic record
Abstract
Thank you very much Mr. Minister for your kind words of introduction.My thanks also go to the Conference of Defence Associations and its Institute represented here by General Silva and Colonel Blakeley and to all of you for being present tonight.I am very touched at receiving this prestigious award.As I look around this room and see so many friends I am also very grateful that you would come in such numbers.Nous sommes encore au mois de novembre, le mois qui represente Ia fin de Ia premiere Grande Guerre et le mois ou traditionellement nous, citoyens canadiens, regardons en arriere et nous souvenons de ceux et celles qui nous ont precedes au service de Ia nation et commemorons leur sacrifice tant a Ia guerre que durant les operations de maintien de Ia paix d'aujourd'hui.En meme temps, nous nous preparons tous a cette grande periode de rejouissances qu'est Noel et le Nouvel An, periode qui, en particulier cette annee represente Ia fin d'un siecle et celle d'un millenaire.Coincidence ou non, cette periode de 1 'annee se prete a Ia meditation sur ce qui etait, ce qui est main tenant et sur ce qui nous attend dans le futur.That we need to remember the past has been well explained, not only by historians, but also ©Canadian Military History, Volume 9, Number 1.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.180 | 0.053 |
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".