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Record W4385691193 · doi:10.1017/9781316459768

Computing the Climate

2023· book· en· W4385691193 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

VenueCambridge University Press eBooks · 2023
Typebook
Languageen
FieldEnvironmental Science
TopicScience and Climate Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsClimate modelClimate changeAtmosphere (unit)Earth system scienceIdeal (ethics)Climate systemClimate scienceTracingGreenhouse gasComputer scienceMeteorologyGeographyEnvironmental scienceHistoryPolitical scienceGeology

Abstract

fetched live from OpenAlex

How do we know that climate change is an emergency? How did the scientific community reach this conclusion all but unanimously, and what tools did they use to do it? This book tells the story of climate models, tracing their history from nineteenth-century calculations on the effects of greenhouse gases, to modern Earth system models that integrate the atmosphere, the oceans, and the land using the full resources of today's most powerful supercomputers. Drawing on the author's extensive visits to the world's top climate research labs, this accessible, non-technical book shows how computer models help to build a more complete picture of Earth's climate system. 'Computing the Climate' is ideal for anyone who has wondered where the projections of future climate change come from – and why we should believe them.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.042
Threshold uncertainty score0.969

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.024
GPT teacher head0.212
Teacher spread0.188 · 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