Some Thoughts about Private Harold L. Green of the Scout Platoon, First Battalion, the Royal Hamilton Light Infantry
Bibliographic record
Abstract
Editor’s note: On 12 August the RHLI was ordered to advance towards Clair Tizon. At a small crossroads near the abandoned hamlet of Barbery, the Regiment met German resistance. As the unit moved through the wheatfields, it encountered German infantry and armour. The maelstrom that engulfed the Rileys was later described as “the most intense mortaring and shelling the unit ever witnessed.” All day long the Battalion endured German counterattacks designed to keep the Falaise pocket open. At dusk the Germans withdrew leaving the field to the Canadians. The cost of the battle was high—20 men killed and 100 wounded. Doug Shaughnessy was detailed to help recover the men killed during the battle. While carrying out this duty, Shaughnessy had the heartbreaking experience of discovering the body of his best pal and scout buddy, Private Harold L. Green, who had been felled by a German machine gunner.\nShaughnessy states that this tribute to Harry was “written by a soldier who probably knew him better than most and was his closest friend at least in the last year of his life and who buried him in a soldier’s grave on a hill top above the town of Bretteville-sur-Laize in Normandy.”
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.042 | 0.012 |
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".