“Mud, mud, mud to the right of us”: How Canadian Reconnaissance Regiments Adapted to the Terrain of the Scheldt Estuary
Bibliographic record
Abstract
During the Battle of the Scheldt, Canadian reconnaissance regiments played a crucial role in gathering information for their attached divisions and participating in combat operations. To be effective in this role, these regiments needed to maintain a high level of mobility and be adept at operating at or beyond the divisional front. As First Canadian Army moved into the Scheldt Estuary in autumn 1944, the reconnaissance regiments’ mobility and efficiency were challenged by the difficult terrain. These regiments therefore developed new techniques to allow them to continue fulfilling their role despite these challenges, including the adoption of new weaponry and operating on their own initiative.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.055 | 0.027 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".