Mapping a continent: historical atlas of North America, 1492-1814
Bibliographic record
Abstract
In March 1493, Christopher Columbus returned a long voyage to the west, convinced he had reached India. In truth, an immense continent, then absent any map, had blocked his path. A formidable barrier separating Europe Asia, North America became a coveted land, attracting sailors, missionaries, trappers, soldiers and scientists. Seeking not only the Vermilion Sea but also fish, beavers, and precious metals, they crossed rivers and trekked through portages, forests, and mountains. With the help of Indians they unlocked the secrets of this terra incognita. Art, scientific papers, and maps provide essential witness to this quest for knowledge that allowed Columbus, Auchagac, Champlain, Franquelin, Thomspon, Mackenzie, and Lewis and Clark to take the measure of America. For three centuries, motivated by the goal of finding a nautical route to the Pacific Ocean and there the Orient, European explorers surveyed and mapped the large territory, exploring every body of water, the tiniest bays to the greatest rivers, and pushing deeper into the interior. Three hundred years almost to the day after Columbus' first voyage, Alexander Mackenzie reached the Pacific Ocean from Canada, by land, 22 July 1793. In 1805, spurred on by Jefferson, the Lewis and Clark expedition crossed the continent the Missouri-Mississippi delta to where the Columbia River flows into the Pacific Ocean. The continent's measure had been taken.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.028 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".