Bibliographic record
Abstract
Ellesmere Island today is a destination only to a particular type of tourist: rich enough to afford the most exclusive of package tours, and hardy (or ascetic) enough to yearn for the spiritual purity of an icy wasteland, rather than the sensual pleasures of a Mediterranean seashore. The island is large—twice the size of Iceland. Yet, its largest settlement, Grise Fjord (or, in the local Inuktitut language, Aijuittuk—‘the place that never thaws’) has but some 140 souls—while its smallest, Eureka, bizarrely but somehow appropriately, was listed in 2006 as having precisely none. Squeezed between northern Canada and Greenland, Ellesmere Island is well within the Arctic Circle, and its northern tip is not much more than 700 kilometres from the North Pole. A land of mountains, fjords, glaciers, and ice-fields, it has been dubbed ‘the horizontal Everest’. In the short summer, the Sun never leaves the sky, and temperatures might, on brief sunny days, exceed 20 °C. When the winter months come, the Sun never rises, and temperatures drop below –40 °C. The only tree that can grow, here and there, is the dwarf Arctic willow, usually knee-high, while the mammals—musk ox, caribou, seals—have attracted Inuit hunters for some 4,000 years (and more lately, Viking explorers too). It was the handsomely whiskered First Lieutenant Adolphus Washington Greely (1844–1935) of the United States Army who discovered the ancient forest that had lain there, deeply buried, for fifty million years, a forest as expressive of bygone glories as any Arthurian legend. As part of the First International Polar Year, in 1882, he had been given charge of a party of soldiers, and tasked with making magnetic and meteorological measurements in the far north. They explored the Greenland coast, and traversed Ellesmere Island from east to west, stumbling upon the forest in the course of these journeys. The voyage killed most of his men, and almost killed him. When the relief crews arrived, two years late (the expedition had not been ideally planned) only six men, including Greely, were left alive. They had survived—just—by eating their own boots and, it seems, the remains of their dead colleagues.
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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 | 0.001 | 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 | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.372 | 0.195 |
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".