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Record W4403370367 · doi:10.1093/yiel/yvae008

Arctic

2023· article· en· W4403370367 on OpenAlexaff
David VanderZwaag

Bibliographic record

VenueYearbook of International Environmental Law · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsArcticThe arcticEnvironmental scienceGeographyOceanographyGeology

Abstract

fetched live from OpenAlex

This year, initiatives relevant to Arctic environmental protection occurred through the Arctic Council and other international cooperative avenues.These include advancing implementation of the Agreement to Prevent Unregulated High Seas Fisheries in the Central Arctic Ocean, working through the International Maritime Organization (IMO) to further address Arctic shipping issues, and seeking environmental cooperation through the Barents Euro-Arctic Council.(2) ARCTIC COUNCIL Arctic Council activities were substantially curbed this year due to Russia's continued military activities against Ukraine.The thirteenth meeting of the Arctic Council was held in May in Salekhard, Russian Federation, and online, but only a one-page statement, rather than a ministerial declaration, was issued on 11 May.Arctic States and Permanent Participants acknowledged the commitment to safeguard and strengthen the Arctic Council and agreed to continue Council activities for 2023-5 based on previous work plans, which included the Reykjavik Ministerial Declaration of May 2021 and the Arctic Council Strategic Plan 2021-30.The statement further acknowledged the Russian Federation's conclusion of a second term as chair of the Council and accepted Norway's offer to act as chair of the Council in 2023-5 and to host the fourteenth meeting of the Council in 2025.In August, consensus was reached to allow Arctic Council working groups to advance project activities via written procedures but still not through official virtual meetings (<https://www.arcticcouncil.dspace7.dspace-express.com/ handle/11374/3145> and Statement (11 May 2023) <https:

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.289
Teacher spread0.272 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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