Otto Sverdrup shortly after his return from the Canadian Arctic Archipelago in 1902. This year, 1974, marks the one hundred and twentieth anniversary o f his birth. the Russian Imperial Navy
Bibliographic record
Abstract
Otto Sverdrup, one of Norway’s greatest explorers, is usually remembered for his participation, as captain of Fram, in Nansen’s memorable drift of 1893-96, and for his remarkably successful exploratory expedition in 1898-1902, again in Fram, to what are now the Queen Elizabeth Islands. Such obviously Scandinavian names as Axel Heiberg Island, Grise Fiord, and Slidre Fiord, bestowed by Sver-drup, testify to his achievements in that area. But several later arctic exploits of Otto Sverdrup’s, although in some ways ranking equally as high as the better known expeditions, have achieved relatively little renown. One of these was his leadership of the search-and-rescue expedition aboard Eklips in the Kara Sea in 1914-15, described in detail by L. M. Staroka-domskiy (1959) on whose account this article is largely based. Although one can not state categorically that Sverdrup’s presence and experience saved human life, it is fair to say that had it not been for Sverdrup, and had ice conditions in the summer of 1915 been more severe, the Russian Imperial Navy might have expe-rienced a major disaster.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".