MétaCan
Menu
Back to cohort
Record W1993853324 · doi:10.1029/2012eo230009

Strategies to deliver information on regional climate changes to communities

2012· article· en· W1993853324 on OpenAlexaffabout
Hans von Storch, Francis W. Zwiers

Bibliographic record

VenueEos · 2012
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsContextualizationClimate changeContext (archaeology)Adaptation (eye)Sociology of scientific knowledgeEnvironmental resource managementRegional scienceGeographyService (business)Political economy of climate changePolitical scienceEnvironmental planningBusinessSociologyEnvironmental scienceSocial scienceEcologyComputer science

Abstract

fetched live from OpenAlex

Regional Climate Services Workshop 2011; Victoria, British Columbia, Canada, 21–23 November 2011 Recognizing that adaptation to both current and projected climate variability and change is best undertaken locally and regionally, a recent workshop was convened to analyze how regional climate services are delivered. Regional climate service organizations facilitate efficient adaptation by interacting with local and regional stakeholders. Regional and local contextualization of observed and projected climate change is an important issue in large federally organized countries like Canada and Germany (as opposed to centralized countries). Exchanging knowledge and knowledge needs on changing climate regimes takes place in societal context within which scientific knowledge is challenged by various interest‐ led knowledge claims about climate change and its societal significance.

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)0.011
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.462
Threshold uncertainty score0.918

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0040.001
Scholarly communication0.0050.003
Open science0.0020.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0400.005

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 head0.231
GPT teacher head0.402
Teacher spread0.171 · 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 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
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueEosSame topicdemographic modeling and climate adaptationFrench-language works237,207