Mark Lardas, B-25 Mitchell vs. Japanese Destroyer. Battle of the Bismarck Sea 1943 by Robert L. Shoop
Bibliographic record
Abstract
The Northern Mariner / Le marin du nord of the war brought a technical efficiency and capability to naval air power that rapidly eclipsed the awesome, traditional power of the battleship.The aircraft carrier had emerged as the new capital ship.Nevertheless, as a visual manifestation of sheer power, the battleship had an aura of omnipotence which the carrier could never quite match" (136).Yamato is a slim book with an abundance of excellent illustrations.Knowles provides an assortment of technical information about the ship assembled in a coherent way and background data to place the Pacific conflict in its historical perspective.The author vividly narrates the battles of Leyte Gulf and Ten-Go mostly from the Japanese standpoint, but also integrating it with the American counter-narrative or viewpoint.This is different from the classic Samuel Eliot Morrison Pacific Theatre Second World War book and the more recent and similarly compelling trilogy by Ian Toll.A major problem is the use of only one confusing map to illustrate the locations of the warship manoeuvres and counter-manoeuvres in the naval battles among the participants.Still, Daniel Knowles's book is a valuable addition to the library of maritime historians, especially those interested in the design, building, and demise of the largest and most powerful battleship(s) to ever put to sea.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".