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Record W4236516142 · doi:10.14430/arctic4743

Arctic Observing Summit (AOS) 2018 Statement and Call to Action

2018· article· en· W4236516142 on OpenAlexvenueaboutno aff
AOS Executive Committee Members

Bibliographic record

VenueARCTIC · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSummitCall to actionArcticStatement (logic)Action (physics)The arcticOceanographyGeographyPhysical geographyPolitical scienceGeologyBusinessAdvertisingPhysicsLaw

Abstract

fetched live from OpenAlex

T he Arctic observinG summit (AOS) is an international conference that aims to provide community-driven, science-based guidance for the design, implementation, coordination, and operation of a sustained (decades-long) international network of Arctic observing systems.The AOS provides a platform for addressing urgent and broadly recognized needs of Arctic observing across all components of the Arctic system, including the human component.The AOS fosters communication and international collaboration and coordination of long-term observations to improve our understanding of system-scale Arctic change and responses to it.The AOS is an international forum that aims to optimize resource allocation, minimize information gaps, and avoid duplication by coordinating efforts and exchanging information among researchers, agencies, northern peoples, non-governmental organizations, the private sector, and others involved or interested in longterm observing activities.The 2018 AOS was the fourth Summit, following those held in Vancouver, Canada, in 2013, Helsinki, Finland in 2014, and Fairbanks, USA, in 2016.Summits are structured thematically, building iteratively on previous outcomes and recommendations.In 2018, delegates focused on the business case for a pan-Arctic observing system.The following statement summarizes the main conclusions, recommendations, and call to action from the Arctic Observing Summit 2018 held in Davos, Switzerland, on 24-26 June 2018.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.967

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.080
GPT teacher head0.374
Teacher spread0.294 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2018
Admission routes2
Has abstractyes

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