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Record W2085571651 · doi:10.1029/2012eo180011

Climate change: Global risks, challenges and decisions

2012· article· en· W2085571651 on OpenAlexaff
Gordon McBean

Bibliographic record

VenueEos · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsWestern University
Fundersnot available
KeywordsClimate changeEquity (law)Greenhouse gasPolitical scienceContext (archaeology)Sustainable developmentGlobal warmingParagraphUnited Nations Framework Convention on Climate ChangeNatural resource economicsConventionBusinessEnvironmental planningEnvironmental resource managementEnvironmental scienceGeographyEconomicsLawKyoto ProtocolEcology

Abstract

fetched live from OpenAlex

In 2009, world leaders at the 15th Conference of the Parties under the United Nations Framework Convention on Climate Change agreed to the Copenhagen Accord, which states in the opening paragraph, “To achieve the ultimate objective of the Convention to stabilize greenhouse gas concentration in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system, we shall, recognizing the scientific view that the increase in global temperature should be below 2 degrees Celsius, on the basis of equity and in the context of sustainable development, enhance our long‐term cooperative action to combat climate change.” This book addresses the key elements of that statement: On the basis of analyses of climate science, what is dangerous? Where does the 2°C come from? What are possible response measures, and can we hold at 2°C? What are the critical impacts and needs for adaptation? The book presents these issues in the basis of equity and in the context of sustainable development and, most important, talks about the challenges.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.008
Scholarly communication0.0120.009
Open science0.0010.004
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0070.002

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 head0.454
GPT teacher head0.341
Teacher spread0.113 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations41
Published2012
Admission routes1
Has abstractyes

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