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Record W2330660439 · doi:10.1093/maghis/19.6.48

The Struggle for Fishing Resources in the Pacific Northwest in the Late Twentieth Century

2005· article· en· W2330660439 on OpenAlexaboutno aff
R. E. Neunherz

Bibliographic record

VenueOAH Magazine of History · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsFrontierFishingGeographyResource (disambiguation)Settlement (finance)ArchaeologyColonialismPolitical scienceLaw

Abstract

fetched live from OpenAlex

From the beginnings of the European penetration of the areas eventually to comprise the United States and Canada, first explorers, then resource seekers, and finally settlers commonly reported enthusiastically about the abundant riches and enormous extent of the lands they encountered. Nothing in their experiences in Europe prepared them for the overwhelming size of the land, forests that seemed to stretch endlessly westward, abundant animal life, rivers and lakes that drained an immense interior, and richly fertile soils. Almost immediately, a belief blossomed in the minds of early settlers that America was blessed with “inexhaustible resources.” As settlement moved inland and up river valleys during the colonial period, farmers found fresh soils to exploit for tobacco and wheat, and plentiful fur resources lured men beyond the Appalachians. After independence, as settlement moved westward, Americans found rich soils in the Old Northwest and Old Southwest, vast expanses of prairie lands, enormous forests of pine, and mineral wealth beyond most people's imagination: gold, silver, iron, copper, and finally oil. While Native peoples had occupied these lands for thousands of years, these resources, which so amazed Europeans, had been only lightly exploited. Indeed, one can argue that much of the extraordinary wealth and economic power accumulated in the United States by the early twentieth century can be explained by the aggressive exploitation of the cheap, abundant, and hardly-used resources available in North America.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.009
GPT teacher head0.191
Teacher spread0.182 · 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 designNot applicable
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
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueOAH Magazine of HistorySame topicAmerican Environmental and Regional HistoryFrench-language works237,207