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Record W2591626713 · doi:10.5539/jpl.v10n2p264

Construction of Artificial Islands in Southern Coast of the Persian Gulf from the Viewpoint of International Environmental Law

2017· article· en· W2591626713 on OpenAlexvenueno aff
Karang Ghaffari, tavakkol habibzadeh, Mortaza Najafi Asfad, Reza Mousazadeh

Bibliographic record

VenueJournal of Politics and Law · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
Fundersnot available
KeywordsPersianDamagesLand reclamationInternational lawGeographyEnvironmental impact assessmentEnvironmental protectionMarine conservationEnvironmental lawEnvironmental resource managementOceanographyLawEnvironmental planningPolitical scienceEnvironmental scienceArchaeologyGeology

Abstract

fetched live from OpenAlex

Among the rapid and rampant costal developments of Persian Gulf region, creating artificial islands is one of new-emerging and developing phenomena in this area. Extensive activities initiated by The United Arab Emirates and other countries of the southern coast of Persian Gulf to create such islands, have had widespread environmental consequences and have led to the criticism of environmentalists. International environmental law has complied comprehensive rules and regulations in order to protect the environment, in particular, protecting the marine environment. Numerous conventions have focused on the issue of marine environment protection, and have mentioned the obligations and responsibilities of states regarding the damages and pollutions to the environment caused by their developmental activities. Persian Gulf coastal states, which are mostly a member of these conventions, are obliged to observe the environmental obligations and regulations related to their widespread activities in the coasts of Persian Gulf, which often leads to drying the sea and land reclamation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.868

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)0.0000.000

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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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 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

Citations13
Published2017
Admission routes1
Has abstractyes

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