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Record W2351312963

Achievements of the Negotiation on the Intergovernmental Committee Under the Nagoya Protocol (ICNP)

2014· article· en· W2351312963 on OpenAlexaboutno aff
WU Jian-yon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationViewpointsConference of the partiesProtocol (science)Political scienceConventionPublic administrationBusinessEnvironmental planningOperations researchEngineeringGeographyLawMedicine
DOInot available

Abstract

fetched live from OpenAlex

At CBD COP- 10 convened on 29 October 2010,in Nagoya of Japan,a decision was made to establish an Open-ended Ad Hoc Intergovernmental Committee( ICNP),which is charged with the responsibility of preparing for the convention of COP /MOP- 1 for the Nagoya Protocol on access and benefit-sharing(ABS) to discuss the issue of fair and equitable sharing of the benefits arising from the utilization of genetic resources and traditional knowledge. ICNP had already three meetings(ICNP- 1,ICNP- 2 and ICNP- 3) held in June 2011,July 2012 and February 2014,separately,in Canada,India and Korea,discussing problems that may arise when the Protocol goes into effect for implementation. After scrutinizing the viewpoints of various signatory countries and governments of some other countries about some major topics,such as two-year program budgeting,rules of procedure for COP /MOP,global multilateral benefit-sharing mechanisms,ABS information clearing house,capacity building,consciousness raising,procedures and mechanisms for compliance,monitoring and reporting,and model contract clauses,etc.,the paper has summarized progresses of the meetings,analyzed comprehensively achievements of the negotiations on these topics,and talked in depth about positive effects of the implementation of the Nagoya Protocol may generate and challenges it may face.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.721
Threshold uncertainty score0.996

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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 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
Published2014
Admission routes1
Has abstractyes

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