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Record W4237173384 · doi:10.1093/yiel/yvx044

E. Iceland

2016· article· en· W4237173384 on OpenAlexaboutno aff
Davíð Örn Sveinbjörnsson

Bibliographic record

VenueYearbook of International Environmental Law · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsKyoto ProtocolGreenhouse gasUnited Nations Framework Convention on Climate ChangeClimate changeAgency (philosophy)ConventionGovernment (linguistics)Montreal ProtocolIcelandicTourismArcticEnvironmental protectionPolitical scienceEnvironmental resource managementEnvironmental scienceGeographyMeteorologySociologyLawOzone layerEcologySocial science

Abstract

fetched live from OpenAlex

Amid political turbulence in Iceland, including the resignation of the prime minister, an early election, and a relatively difficult formation of a government, there have been some developments in environmental matters. The effects of growing tourism in Iceland, climate issues, and the Arctic were at the forefront of developments in the year 2016. In April, the Icelandic Environmental Agency announced that the reporting and review process for the first commitment period of the Kyoto Protocol to the United Nations Framework Convention on Climate Change (UNFCCC) was complete and that Iceland had fulfilled its commitments for the period of 2008–12. While the collective goal for the reduction of greenhouse gas emissions under the protocol was 5 percent, Iceland’s commitment was not to increase emissions above 10 percent compared with 1990 levels. Iceland also availed itself of the provisions of Decision 14/CP.7, allowing Iceland to report on the industrial processing of carbon dioxide emissions separately and not to include them in national totals (further information on the report can be found at < http://unfccc.int/kyoto_protocol/reporting/true-up_period_reports_under_the_kyoto_protocol/items/9049.php>). Iceland remains committed to the reduction of greenhouse gas emissions and submitted its intended nationally determined contribution to the UNFCCC on 30 June 2015, where it aims to be part of a collective delivery by European countries to reach a target of 40 percent reduction of greenhouse gas emissions by 2030 compared to 1990 levels.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.264
Teacher spread0.253 · 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.

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
Published2016
Admission routes1
Has abstractyes

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