Bibliographic record
Abstract
We'll get crushed by the ocean but it will not get us wet. —Isaac Brock, “Invisible” (2007) “There is no Sea With Which Our Age is So Imperfectly Acquainted as the Frozen Ocean,” Wrote the Eighteenth-Century Russian hydrographer Gavriil Sarychev, “and no empire which has more powerful motives and resources for extending its information, in this quarter, than Russia” (iii). Russia's Great Northern Expedition of the 1730s and later expeditions, like Sarychev's in 1785, mapped the shores of the Arctic Ocean across continental Asia, an impressive feat by any century's standards. Meanwhile, the American shores of the Arctic Ocean remained entirely unknown to the European empires (England, France, Spain) most interested in passing to and from the Pacific and Atlantic oceans via the Northwest and Northeast passages. Alexander MacKenzie, Samuel Hearne, and John Franklin, each traveling with native people, walked thousands of miles to reach the Frozen Ocean, leaving in their wake the occasional human disaster and an unimpeachable record of publishing successes, like MacKenzie's Voyages from Montreal to the Frozen Ocean (1801) and Franklin's Narrative of a Journey to the Shores of the Polar Sea (1824).
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".