MétaCan
Menu
Back to cohort
Record W2323867912 · doi:10.1061/9780784413548.115

Early Exploration and Mapping of the Columbia River

2014· article· en· W2323867912 on OpenAlexaboutno aff
David Gilbert

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2014 · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicArchaeology and Natural History
Canadian institutionsnot available
Fundersnot available
KeywordsGeographerColumbia universityBayArchaeologyGeographyGeorge (robot)OceanographyHistoryCartographyGeologyArt history

Abstract

fetched live from OpenAlex

Long before the arrival of European explorers in the Pacific Northwest region of the United States, the Columbia River was an important trade route and source of salmon for Native American tribes. Because they published no maps, they alone knew the complex topography of the Columbia River basin. Early Spanish maps in 1775 showed the mouth of the Columbia as Entrada de Hecita, named after the explorer, Bruno de Hecita, but he did not explore upstream. In 1792 American captain and fur trader, Robert Gray, became the first known explorer to actually cross the very dangerous bar at the mouth of the Columbia River. That same year British Captain George Vancouver, during his 1791-1795 voyage to the Pacific Northwest, sent one of his ships across the bar to map 100 miles upriver to what is now Vancouver, Washington. The Lewis and Clark Expedition of 1804-1806 later explored the Columbia from its confluence with the Snake River near Pasco, Washington, downstream to its mouth at present day Astoria. But from 1806 to 1811, it was David Thompson, a British surveyor and geographer for the North West Company formerly of the Hudson Bay Company, who sought and succeeded to unravel the mystery of the remaining unknown three-quarters of the Columbia's course. These various explorations by several nations supported claims to the lands now making up the Pacific Northwest region of the United States and the province of British Columbia, Canada. This paper describes the work of these various explorers and the maps resulting from their surveys.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.188
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueWorld Environmental and Water Resources Congress 2014Same topicArchaeology and Natural HistoryFrench-language works237,207