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Record W4393455838 · doi:10.5281/zenodo.7652558

Lunar eclipses illuminate timing and climate impact of medieval volcanism

2023· dataset· en· W4393455838 on OpenAlexaff
Sébastien Guillet, Christophe Corona, Clive Oppenheimer, Franck Lavigne, Myriam Khodri, Francis Ludlow, Michael Sigl, Matthew Toohey, Paul S. Atkins, Zhen Yang, T. Muranaka, Nobuko Horikawa, Markus Stoffel

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typedataset
Languageen
FieldSocial Sciences
TopicHistorical Geography and Cartography
Canadian institutionsUniversity of Saskatchewan
FundersHorizon 2020 Framework ProgrammeSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsVolcanismAstrobiologyGeologyEarth scienceEnvironmental sciencePaleontologyPhysicsTectonics

Abstract

fetched live from OpenAlex

This repository contains all the data and codes needed to reproduce the results and figures from the article "Lunar Eclipses Illuminate Timing and Climate Impacts of the Middle Ages" published in Nature.<br> <br> For more information, we refer the user to the readme file entitled "Guillet_et_al_Nature2023_Readme.txt".<br> <br> If you have any queries, please feel free to contact us: sebastien.guillet@unige.ch<br> <br> Thank you very much ;-)

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.015
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.003

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 head0.041
GPT teacher head0.307
Teacher spread0.266 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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