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Record W6917515642 · doi:10.57757/iugg23-4619

Phase 4 of PAGES 2k: Hydroclimate of the common era

2023· article· en· W6917515642 on OpenAlexaff

Bibliographic record

VenuePublication Database GFZ (GFZ German Research Centre for Geosciences) · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProxy (statistics)Earth system scienceContext (archaeology)Global climateInteroperabilityScope (computer science)Global network

Abstract

fetched live from OpenAlex

<!--!introduction!--><b></b> The climate of the past two thousand years (2k) provides context for current and future changes, and as such is vital for developing our understanding of the modern climate system. <br><br>Previous PAGES 2k network phases emphasized temperature reconstructions, fundamentally improving our understanding of global climate changes over the Common Era. These reconstructions were featured in the Summary for Policymakers of the IPCC’s Sixth Assessment Report. Integration of this data with state-of-the-art Earth systems models, proxy system models and data assimilation yielded a more comprehensive understanding of the associated physical drivers and climate dynamics. &nbsp; Phase 4 of the PAGES 2k Network paves the way for a new level of understanding of the global water cycle. Our aim is to reconstruct hydroclimatic variability over the Common Era, from local to global spatial scales, at sub-annual to multi-centennial time scales, developing a process-level understanding of past hydroclimate events and variability. Our multi-faceted approach includes (1) developing new hydroclimate syntheses that are well-suited for data-model comparisons, (2) improving the interoperability and scope of existing data and model products, and (3) facilitating the translation of our science into evidence-based policy outcomes. In this presentation, we describe the progress to date, and focus the discussion on what a suitable hydroclimate proxy database should look like, both from the perspective of what data are available, and from what is a good target for model evaluation, using different regions of the world as case studies. &nbsp; PAGES 2k is an open community, and you are welcome to get involved.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.087
GPT teacher head0.380
Teacher spread0.293 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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