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Record W2559561279 · doi:10.5194/cp-12-2107-2016

The 1430s: a cold period of extraordinary internal climate variability during the early Spörer Minimum with social and economic impacts in north-western and central Europe

2016· article· en· W2559561279 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClimate of the past · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsYork UniversityQueen's University
Fundersnot available
KeywordsFamineSubsistence agricultureClimate changeClimatologyProxy (statistics)GeographyForcing (mathematics)Climate modelEnvironmental scienceAgricultureOceanographyGeologyArchaeology

Abstract

fetched live from OpenAlex

Abstract. Changes in climate affected human societies throughout the last millennium. While European cold periods in the 17th and 18th century have been assessed in detail, earlier cold periods received much less attention due to sparse information available. New evidence from proxy archives, historical documentary sources and climate model simulations permit us to provide an interdisciplinary, systematic assessment of an exceptionally cold period in the 15th century. Our assessment includes the role of internal, unforced climate variability and external forcing in shaping extreme climatic conditions and the impacts on and responses of the medieval society in north-western and central Europe.Climate reconstructions from a multitude of natural and anthropogenic archives indicate that the 1430s were the coldest decade in north-western and central Europe in the 15th century. This decade is characterised by cold winters and average to warm summers resulting in a strong seasonal cycle in temperature. Results from comprehensive climate models indicate consistently that these conditions occurred by chance due to the partly chaotic internal variability within the climate system. External forcing like volcanic eruptions tends to reduce simulated temperature seasonality and cannot explain the reconstructions. The strong seasonal cycle in temperature reduced food production and led to increasing food prices, a subsistence crisis and a famine in parts of Europe. Societies were not prepared to cope with failing markets and interrupted trade routes. In response to the crisis, authorities implemented numerous measures of supply policy and adaptation such as the installation of grain storage capacities to be prepared for future food production shortfalls.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.579

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 studies0.0000.001
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 head0.207
Teacher spread0.198 · 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