Bibliographic record
Abstract
Grit, valor, and perseverance. These are the qualities shared by great explorers throughout history and around the world, from the Vikings to David Livingstone. Historical Atlas of Expeditions follows the journeys of these adventurers. No one knew what lay ahead with each new endeavor. Some explorers met a grim fate, while others broke boundaries to help form the world as we know it. Their tales, taken from around the globe, provide an exciting look at this age of discovery. This title features: in-depth information of famous explorers, their overland expeditions, and the people they met; detailed, colorful illustrations of the various machines explorers used over the centuries; and, timelines and maps for each section. The coverage includes: early explorers, such as Alexander the Great and St. Brendan, who is thought to have reached the New World; expeditions of the Middle Ages, including those by Leif Erikson and Hernando Cortes; and, explorers and their discoveries in Asia, Africa, and the Americas from 1600 - including George Vancouver, Mary Kingsley, Charles Darwin, and Meriwether Lewis and William Clark.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".