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Record W2910799893 · doi:10.5194/cp-2019-2

Drought during early European exploration and colonization of North America, 1500–1610CE: A comparison of evidence from the archives of societies and the archives of nature

2019· article· en· W2910799893 on OpenAlexaboutno aff
Sam White

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsHistorical recordGeographyHistoryDendrochronologyClimatologyArchaeology

Abstract

fetched live from OpenAlex

Abstract. This article presents the historical evidence concerning the occurrence of drought in North America from 1510–1610CE based on a comprehensive review of original written records concerning all early European expeditions into the present US and Canada. It compares this evidence from the archives of societies with maps and time series of drought generated from the tree ring-based North American Drought Atlas (NADA). This comparison demonstrates the reliability of early colonial historical records as sources of evidence concerning drought, as well as the applicability of the NADA to the scale of local and regional human historical events. The comparison further verifies the occurrence and societal impacts of certain major droughts previously identified in dendroclimatological studies, but suggests that some summer hydrological deficits indicated in the tree ring record reflect a deficiency of summer rather than winter precipitation. Finally, this review of evidence from both the archives of societies and archives of nature highlights the extraordinary challenges faced by early European explorers and colonists in North America due to climatic variability in an already unfamiliar and challenging environment.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.977
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.025
GPT teacher head0.241
Teacher spread0.216 · 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 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

Citations2
Published2019
Admission routes1
Has abstractyes

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