Steve R. Dunn, Battle in the Baltic: The Royal Navy and the Fight to Save Estonia & Latvia 1918-20 by Chris Madsen
Bibliographic record
Abstract
The Northern Mariner / Le marin du nord the relative dominance of the offense and defense.If modern analysts are to be believed, and technological change is faster now than it has ever been in the past, that rebalancing might create rapid changes in the military dynamics of the Indo-Pacific and affect defenses against the threat of the missiles.Doyle and Herzinger's Carrier Killer offers a thoughtful, thorough, and clear-eyed analysis of the possibilities created by China's deployment of antiship ballistic missiles.Rather than "silver bullet" weapons that change the balance of power in the Pacific, the authors demonstrate that these weapons are complex and unproven.At the same time, they certainly represent a threat to the safety of American aircraft carriers and the power projection mindset of American naval thinking.The vulnerability of the carriers has been a subject of debate for almost a hundred years.The authors do an excellent job of placing these new weapons in their technological, military, diplomatic, and informational context, backed up with excellent illustrations, maps and historical background.As a quick primer on important developments in the Pacific world, Carrier Killer offers naval professionals, contemporary analysts, historians, and students a valuable resource to help them understand the complexity of today's Sino-American naval competition.Benjamin "BJ" Armstrong Annapolis, Maryland Steve R. Dunn.Battle in the Baltic: The Royal Navy and the Fight to Save Estonia
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".