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Record W2098941117 · doi:10.1093/yiel/20.1.xvii

From the Editors-in-Chief

2009· article· en· W2098941117 on OpenAlexaff
Ole Kristian Fauchald, David Hunter, Xi Wang

Bibliographic record

VenueYearbook of International Environmental Law · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsYearbookPublishingArcticPolitical scienceThe arcticClimate changeLibrary scienceOperations researchLawComputer scienceEngineeringGeologyOceanography

Abstract

fetched live from OpenAlex

During the past few years, we have discussed the possibility of publishing an electronic version of the Yearbook, including making available past volumes. We are happy that we have reached agreement with Oxford University Press on publication of a web-based version of the Yearbook. Easy access to the vast amount of information and analysis that has been collected and published in the Yearbook’s twenty volumes will provide a valuable research tool for those who work with, study, or have general interest in international environmental law and policy. We hope electronic publishing of this material will provide significant support to the implementation, clarification, and further development of treaty-based and customary international environmental law. The 2009 volume of the Yearbook addresses polar regimes. Polar regimes face a number of challenges due to climate change, current levels of exploration and exploitation of natural resources, increased interest in polar research, and increases in tourism. Our objective has been to promote studies of the origin and development of the legal regimes that have been established to address these and other challenges in Antarctica and in the Arctic region.

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)0.005
metaresearch head score (Gemma)0.028
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.196
Threshold uncertainty score0.657

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0090.007
Open science0.0030.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.1960.210

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 head0.005
GPT teacher head0.221
Teacher spread0.216 · 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
GenreEditorial

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

Citations1
Published2009
Admission routes1
Has abstractyes

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