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Record W2499843074

The Tokyo Mou: Its Implications for Taiwan

2016· article· en· W2499843074 on OpenAlexaboutno aff
Chen-Ju Chen

Bibliographic record

VenueAustralian and New Zealand Maritime Law Journal · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPort (circuit theory)JurisdictionState (computer science)LawControl (management)Political scienceAsia pacificInternational lawInternational tradeBusinessEngineeringManagementEconomicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

This paper, based on the maritime jurisdiction of port State established under the international law of the sea, looks into the developments of port State control and regional MoUs on port State control. Focused is the Tokyo MoU in the Asia-Pacific region and further investigated is how Taiwan reflects the establishment of the Tokyo MoU. Concluded is that—though in terms of inspection effectiveness—there are still various spaces for improvement, especially as a non-Member State of IMO and regional MoU, Taiwan—via the bilateral agreement with Canada—established its port State control system and can thus still be a model for entity sui generis to enforce port State control.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.412

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.008
Scholarly communication0.0070.004
Open science0.0010.004
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0070.000

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 head0.023
GPT teacher head0.274
Teacher spread0.250 · 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
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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