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Record W4205291952 · doi:10.31227/osf.io/qsngx

Jaringan Transportasi dan Pengembangan Destinasi Pariwisata di Kota Cirebon

2019· article· en· W4205291952 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsIndex (typography)TourismGeographyTransport engineeringComputer scienceEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

Tourism development needs a good accessibility to support regional connectivity. This study aims to analyse the role of transportation network for tourism destination development in the Cirebon City. This research uses GIS-based network spatial analysis to obtain road network system components in each sub-district, thus transportation indices value which includes alpha index, beta index, Gama index, eta index and road density network. The results shows The Cirebon City has low-medium connectivity and accessibility with alpha index 0.1323, beta index 1.2608, Gama index, 0.4221, eta index 0.1576 and road network density reach 20.869 km / km2. On sub-district level, Pekalipan is the most accessible region based on all parameters of road network and create a suitable place for tourism development in the Cirebon City.

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.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.277
Teacher spread0.260 · 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