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Record W4399216018 · doi:10.1175/bams-d-24-0138.1

How to Engage and Adapt to Unprecedented Extremes

2024· article· en· W4399216018 on OpenAlexaff
Dominic Matte, Jens Hesselbjerg Christensen, Martin Drews, Stefan Sobolowski, Dominique Paquin, Amanda H. Lynch, Scott Bremer, Ida Engholm, Nicolas D. Brunet, Erik W. Kolstad, Helena Kettleborough, Vikki Thompson, Emanuele Bevacqua, Dorothy Heinrich, S. C. Pryor, Andrea Böhnisch, Frauke Feser, Andreas F. Prein, Erich Fischer, Martin Leduc

Bibliographic record

VenueBulletin of the American Meteorological Society · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversity of GuelphOuranos
FundersDeutsche ForschungsgemeinschaftNordisk MinisterrådNational Science Foundation
KeywordsEnvironmental scienceMeteorologyClimatologyEarth scienceGeologyGeography

Abstract

fetched live from OpenAlex

Exploring Unprecedented ExtremesWhat: Facing the urgent challenge of extreme weather and climate-related events, our societal frameworks for "resilience" and "adaptation" are proving to be insufficient.This paper introduces the "Exploring Unprecedented Extremes" workshop, which was convened to elucidate the research gap in light of such challenges.The workshop tackled a broad spectrum of issues, ranging from assessing out-of-sample climatic events that defy traditional modeling approaches to enhancing the communication of risks and likelihoods associated with such unprecedented events.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.299
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.282
Teacher spread0.261 · 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 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

Citations5
Published2024
Admission routes1
Has abstractyes

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