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Record W1982975884 · doi:10.1029/2006eo520002

The historic fur trade and climate change

2006· article· en· W1982975884 on OpenAlexaboutno aff
Johan C. Varekamp

Bibliographic record

VenueEos · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
FundersNOAA ResearchNational Aeronautics and Space Administration
KeywordsFur tradeExpansionismPeriod (music)ArchaeologyGeographySettlement (finance)BeaverShoreSound (geography)Ancient historyHistoryOceanographyGeologyEconomic historyArtLawPaleontology

Abstract

fetched live from OpenAlex

Why did the Dutch come to the North American shores 400 years ago? Was it wanderlust, expansionist policies, or simply money? The earliest western explorers were the Vikings who, in the 1100s, were able to sail beyond Iceland and Greenland to Newfoundland, because they did so during the Medieval Warm Period. English explorer Henry Hudson, on the other hand, could not find a northern passage to China because the early 1600s were the coldest part of the Little Ice Age and the northern seas were frozen over (Figure 1). After Hudson's third voyage, Dutch merchants contracted several sailors to establish trading posts for beaver pelts in the Americas. Among them was Adriaen Block, who built the first western settlement on the island of Manhattan (New York) after a fire on his ship forced him to over‐winter there in 1614 [Varekamp and Varekamp, 2006]. His 1614 ‘figurative map’ shows Long Island Sound as an estuary, and introduced for the first time the name ‘Niew Nederlandt’ (New Netherlands).

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.000

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 head0.013
GPT teacher head0.175
Teacher spread0.162 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2006
Admission routes1
Has abstractyes

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