Bibliographic record
Abstract
The Mediterranean Sea in World War II was the scene of prolonged and violent naval warfare between the British and the Axis powers, Italy and Germany.The stories of the major squadrons and their admirals have been told, as their actions led directly to the outcomes.Here is the tale of the smallest surface ships, their officers and men who, at the very edge of British sea power in that disputed sea, played vital roles in making possible the successes of the more famous forces.It is told by one of them -a Canadian on loan to the Royal Navy -who took a leading part from the lowest ebb in British fortunes through the struggle against almost overwhelming odds to the climax of the first landings on the continent of Europe.The select 3rd Flotilla of Motor Launches -known as MLs in the United Kingdom and in Canada as "Fairmiles" -was the first to go abroad from the British Isles and the only one sent into the eye of the storm as the larger ships were driven out.A Leaf Upon the Sea is a personal memoir as well as the story of the 3rd Flotilla.Simply, directly and with humour, Stead tells of the "sea" change" wrought in him under fire.Fresh from civilian life, and after sketchy basic training, he picked up the essentials of his new profession under the urgent pressure of events when thrust into command to learn on the job.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.905 | 0.887 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".