Brian E. Walter, Blue Water War: The Maritime Struggle for the Mediterranean and Middle East 1940-1945 by Michael Razer
Bibliographic record
Abstract
Reviews 583 in New York City, became the move star Lauren Bacall.Another chapter is about the sinking of the Titanic, belonging to J.P. Morgan's White Star Line.This seems to be included because it recounts the fate of the prominent Jewish New York banker Isador Strauss and his wife, and various Jewish immigrants who were fellow passengers.The text is not as reliable on matters military.A discussion about how the Cunard liners Lusitania and Mauretania were built suggests potential conversion to heavy rather than auxiliary cruisers.On page 288 the German army is described as circumventing the Maginot Line (not built until the 1930s).Steven Ujifusa writes in an easy journalistic style.His book is illustrated by a section of well-chosen photographs.Despite the title, The Last Ships From Hamburg is mainly not about ships and the Hamburg Amerika Line but Jewish mass migration from eastern Europe to the US between 1881 and 1914 and of how it was facilitated by capable Jewish businessmen on both sides of the Atlantic.Jews constituted 9.4 percent of all immigrants to the USA over these 43 years.This is an interesting popular history which explains why and how this significant population shift happened.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".