Opening an Arctic Escape Route: The Bellot Strait Expedition
Bibliographic record
Abstract
During the second half of the 1950s, Canadian and American vessels surged into the North American Arctic to establish military installations and to chart northern waters. This article narrates the expeditions by the eastern and western units of the Bellot Strait hydrographic survey group in 1957, explaining how these “modern explorers” grappled with unpredictable ice conditions, weather, and extreme isolation to chart a usable Northwest Passage for deep-draft ships. The story also serves as a reminder of the enduring history of US Coast Guard and Navy operations in Canada’s Arctic waters in collaboration with their Canadian counterparts. Au cours de la deuxième moitié des années 1950, des navires canadiens et américains ont envahi l’Arctique nord-américain pour y établir des installations militaires et cartographier les eaux du Nord. Le présent article traite des expéditions des unités est et ouest du groupe de levés hydrographiques du détroit de Bellot en 1957 et explique comment ces « explorateurs modernes » ont été confrontés à des états de glace imprévisibles, à des conditions météorologiques et à un isolement extrême en traçant un passage du Nord-Ouest utilisable pour les navires à forts tirants d’eau. Le récit nous rappelle également l’histoire durable des opérations de la Garde côtière et de la Marine américaines dans les eaux arctiques du Canada en collaboration avec leurs homologues canadiens.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".