Sherman medium tank Canadian, New Zealand and South African armies. Italy, 1943-1945
Bibliographic record
Abstract
"The Sherman tank served with most Allied armies during the Second World War and it is justly famous for the role it played in the Normandy landings and the subsequent drive into Germany. But the part played by the British commonwealth armoured units in the Italian campaign is less well known and in his latest volume in the TankCraft series Dennis Oliver uses wartime photos, extensively researched text and highly-detailed colour illustrations to cover the Sherman tanks of the Canadian, New Zealand and South African armies that battled their way up the Italian peninsula. Although it was often out-gunned by its opponents the Sherman's ability to handle the worst terrain and its mechanical reliability ensured that it was at the forefront of every battle and contributed greatly to the final Allied victory. Examined in this book are both the 75mm armed version and the potent tank killer referred to toady as the Firefly, as well as a number of little-known field modifications. A large part of this work showcases available model kits and aftermarket products, complemented by a gallery of beautifully constructed and painted models in various scales. Technical details as well as modifications introduced during production and in the field are also examined, providing everything the modeller needs to recreate an accurate representation of these historic vehicles. " -- backcover
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".