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Record W2979296780 · doi:10.5194/cp-15-1809-2019

A comparison of drought information in early North American colonial documentary records and a high-resolution tree-ring-based reconstruction

2019· article· en· W2979296780 on OpenAlexaboutno aff
Sam White

Bibliographic record

VenueClimate of the past · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsDendrochronologyObjectivity (philosophy)Historical recordDocumentary evidenceClimate changeGeographyHistoryClimatologyPhysical geographyArchaeologyGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract. Historical documentary records contain valuable information on climate, weather, and their societal impacts during the pre-instrumental period, but it may be difficult to assess the objectivity and reliability of this information, particularly where the documentary record is incomplete or the reliability of the information it contains is uncertain. This article presents a comprehensive review of information relating to drought found in original written records concerning all early European expeditions (1510–1610 CE) into the present-day US and Canada, and compares this information with maps and time series of drought generated from the tree-ring-based North American Drought Atlas (NADA). The two sources mostly agree in the timing and location of droughts. This correspondence suggests that much of the information in these early colonial historical records is probably objective and reliable, and that tree-ring-based drought atlases can provide information relevant to local and regional human historical events, at least in locations where their reconstruction skill is particularly high. This review of drought information from written sources and tree-ring-based reconstructions also 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 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.996

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 studies0.0000.000
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 head0.009
GPT teacher head0.232
Teacher spread0.222 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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