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Record W4382203714 · doi:10.18280/ijsdp.180630

Development of Supporting Technology for Sustainable Spaceport Development

2023· article· en· W4382203714 on OpenAlexvenueno aff
Gunawan Widiyasmoko, Dwinowo Martono, Herdis Herdiansyah, Arif Nur Hakim

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace exploration and regulation
Canadian institutionsnot available
FundersKementerian Pendidikan, Kebudayaan, Riset, dan TeknologiDirektorat Riset and Pengembangan, Universitas IndonesiaUniversitas Indonesia
KeywordsSustainabilitySustainable developmentBusinessEnvironmental planningEnvironmental resource managementEconomic growthPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Spaceports have experienced rapid growth in recent years, but have also faced significant challenges related to management and sustainability, particularly in developing countries like Indonesia.This literature review examines the role of supporting technologies in promoting sustainable spaceport development.Environmental and social concerns, such as disruptions to community activities, emissions, and environmental damage, have emerged as key issues associated with spaceport activities.By promoting open innovation and actor interaction, however, spaceports can foster sustainable development, taking into account infrastructure, social, and economic considerations.This approach aligns with the increasing demand for environmentally friendly practices in the space industry, which can attract investors and support further development.Safety and sustainability considerations should be integrated into every aspect of spaceport operations, given the high risks associated with rocket launches.Overall, this study contributes to a better understanding of the complex interplay between actors and technologies in shaping sustainable spaceport development.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.298
Teacher spread0.279 · 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 designQualitative
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

Citations7
Published2023
Admission routes1
Has abstractyes

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