Bibliographic record
Abstract
Covering the adventures of coastal and ocean explorers who made key discoveries and landmark observations from northern California up the coastline to Alaska during the mid-1700s to the early 1800s, this anthology of primary source journal entries, book excerpts, maps, and drawings enables readers to "discover" the Northwest Coast for themselves. More than 200 years ago, explorers traveled from Central America, Russia, and even Europe to explore the coastline of the American Pacific Northwest, with goals of developing new trade routes, claiming territory for their home countries, expanding their fur trade, or exploring in the name of scientific discovery. This book will take readers to the decks of the great ships and along for the adventures of legendary explorers, such as James Cook, Alejandro Malaspina, and George Vancouver. This book collects primary source materials such as journal entries, book excerpts, maps, and drawings that document how explorers first experienced the unknown Pacific Northwest coast, as seen through the eyes of non-native people. Readers will learn how explorers such as Vitus Bering and Robert Gray used the full extent of their powers of observation to record the landscape, animals, and plants they witnessed as well as their interactions with indigenous peoples during their search for the mythic Northwest Passage. The book also explains how the maritime explorers of this period mapped the remote regions of the Northwest Coast, working without the benefit of modern technology and relying instead on their knowledge of a range of sciences, mathematics, and seamanship—in addition to their ability to endure harsh and dangerous conditions—to produce exceptionally detailed maps.
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 | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 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".