Venner F. Milewski, Jr., Fighting Ships of the U.S. Navy 1883-2019: Volume 3 - Cruisers and Command Ships by Rob Dienesch
Bibliographic record
Abstract
The Northern Mariner / Le marin du nord with high seas and gales.As a spectator, one observes this from afar, but in this work, the reader becomes an onboard spectator struggling along with the seamen, sharing the exhilaration of a race well-run or the disappointment of a hard-fought loss."These sailors were amateurs in the root sense of the word, men who competed for the sheer love of the thing itself, testing their mastery against that of their peers.That's what echoes down through the years-the beauty and danger of a working life under sail, and the pride of the men who did it."(9)Canadian McLaren presents a thoughtful neutral narrative of the hardfought cup race series and their qualifying rounds replete with some stately images along with wonderful action-packed photographs of the ships and their captains.His work, while quite an exciting read in places, is scholarly and includes a wide range of source material gleaned from the local archives of the two maritime cities and other places.Many books and articles have been published about these colourful contests over the years, but this scholarly and non-partisan work is among the best.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".