Bibliographic record
Abstract
Warfare in the Arctic has, for the most part, been a historical oddity. The region boasts few significant cities to capture, small populations, a harsh environment, and little transportation infrastructure. As R. J. Sutherland states in his “Strategic Significance of the Canadian Arctic,” the Arctic offers “no place to go from a military point of view and nothing to do when you got there.” Prior to World War II there was little regular warfare in the circumpolar region, whereas the war itself saw relatively limited action. It was during the Cold War that the Arctic became a recognized area of strategic importance—primarily for strategic bombers and later for nuclear submarines. Although these weapons were never used, an enormous amount of energy and resources went into preparing to fight in the region. The definition of Arctic itself often varies and can be defined on geographic, climactic, or political grounds. This article uses the geographic delimitation of 60 degrees north latitude. This region includes the entire Canadian North, Finland, the Soviet/Russian North, and most of Norway and Alaska. Parts of Alaska south of 60 degrees have been included because they are traditionally characterized as Arctic, whereas warfare on the Baltic Sea has been omitted simply because this area has traditionally not been considered as such.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.015 |
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".