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Record W636152447 · doi:10.22498/pages.17.3.128

Climate variability, forcings, feedbacks and responses: The long-term perspective

2009· article· en· W636152447 on OpenAlexaffabout
Alexandra Börner, Nathalie Dubois, Pete Smith, Natalie St. Amour, Roberto Urrutia

Bibliographic record

VenuePAGES news · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of WaterlooDalhousie University
FundersSwiss ReUniversity of BernSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsProxy (statistics)Perspective (graphical)ClimatologyClimate changePaleoclimatologyClimate scienceGeographyPhysical geographyLibrary sciencePolitical scienceGeologyComputer science

Abstract

fetched live from OpenAlex

The 8th International NCCR Climate Summer School was held in collaboration with PAGES and brought together 74 PhD and post-doctoral students from 16 different countries, mainly from Europe but also Japan, Australia, Chile, Russia, Canada and the USA, as well as18 keynote speakers and workshop leaders. The meeting was held in Grindelwald, a small alpine village located in the Jungfrau region of the Swiss Alps. Participants gathered together to learn about various aspects of paleoclimate science, including proxy reconstructions and modeling, to understand the nature of feedbacks, forcings and their impact on the climate system. The agenda of the meeting, included keynote lectures that were followed by lively discussions, poster sessions, workshops, excursions and group presentations by the participants.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0060.006
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.277
Teacher spread0.255 · 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
Published2009
Admission routes2
Has abstractyes

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