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Record W1821549653 · doi:10.1175/bams-d-15-00003.1

Climate Symposium 2014: Findings and Recommendations

2015· article· en· W1821549653 on OpenAlexaff
Ghassem Asrar, Sandrine Bony, Oliviér Boucher, Antonio J. Busalacchi, Anny Cazenave, Mark Dowell, Greg Flato, Erland Källén, Teruyuki Nakajima, Alain Ratier, Roger Saunders, Julia Slingo, Byung‐Ju Sohn, Johannes Schmetz, Björn Stevens, Peiqun Zhang, Francis W. Zwiers

Bibliographic record

VenueBulletin of the American Meteorological Society · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of VictoriaPacific Institute for Climate Solutions
Fundersnot available
KeywordsAllee effectSociologyDemography

Abstract

fetched live from OpenAlex

Here, the Climate Symposium 2014, organized by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and the World Climate Research Programme (WCRP), with support from the European Commission (EC), European Space Agency (ESA), and other agencies, took place in Darmstadt, Germany, from 13 to 17 October 2014. Around 500 participants from 49 countries attended the event and represented over 200 organizations. Another 500 individuals participated remotely via “live streaming.”

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)0.018
metaresearch head score (Gemma)0.044
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: Review · Consensus signal: none
Teacher disagreement score0.082
Threshold uncertainty score0.215

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.009
Science and technology studies0.0020.001
Scholarly communication0.0070.007
Open science0.0030.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0640.022

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 head0.016
GPT teacher head0.231
Teacher spread0.215 · 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
GenreReview

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
Published2015
Admission routes1
Has abstractyes

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