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Record W4401557120 · doi:10.21776/rechtjiva.v1n2.2

Analisis Pembentukan Perjanjian Internasional untuk Menangani Krisis Penipisan Ozon akibat Emisi Orbital Spacecraft

2024· article· en· W4401557120 on OpenAlexaboutno aff
Brigitta Caecilia Putri Noya, Agis Ardhiansyah, Dony Aditya Prasetyo

Bibliographic record

VenueRechtjiva · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace exploration and regulation
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

This research aims to establish the regulatory framework for emissions generated by orbital spacecraft within the scope of international law, particularly space law and international environmental law, and to formulate a new international agreement that can address the emissions issues arising from orbital spacecraft. In analyzing these issues, the author employs a normative legal method, utilizing legislative and conceptual approaches. The Outer Space Treaty, as the primary agreement regulating all activities in outer space, proves inadequate in addressing the existing problems. Article IX, considered the closest provision to addressing the issue of orbital spacecraft emissions, still falls short and fails to provide a comprehensive solution. The Montreal Protocol, as the principal agreement related to compounds potentially causing ozone depletion, also falls short in evaluating the threat posed by orbital spacecraft emissions due to the limited coverage of regulatory jurisdiction. The regulatory vacuum concerning the impact of orbital spacecraft emissions on ozone depletion needs to be promptly addressed through the creation of a new international agreement. The formation of such an agreement should adhere to existing systematic structures, and the substance of the new agreement must be able to accommodate on-the-ground issues while maintaining an environmental perspective.

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: Empirical
Teacher disagreement score0.385
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.274
Teacher spread0.263 · 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
Published2024
Admission routes1
Has abstractyes

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