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Record W3023168307 · doi:10.25105/psia.v1i1.5963

ANALISIS KAPASITAS TERMINAL PENUMPANG BANDAR UDARA SENTANI DI JAYAPURA

2019· article· id· W3023168307 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

VenueProsiding Seminar Intelektual Muda · 2019
Typearticle
Languageid
FieldEngineering
TopicUrban Transport Systems Analysis
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysics

Abstract

fetched live from OpenAlex

Papua merupakan salah satu daerah yang menjadikan transportasi udara sebagai pilihan utama untuk memenuhi kebutuhan mobilisasi. Seiring meningkatnya kegiatan pariwisata di Jayapura, maka meningkat pula jumlah pengunjung yang datang. Dengan adanya peningkatan jumlah penumpang di Bandar Udara Sentani, terminal penumpang terkadang mengalami penumpukan di beberapa area. Perlu di lakukan evaluasi terhadap kapasitas terminal penumpang yang ada saat ini dan analisa kebutuhan luas terminal penumpang dalam menampung sirkulasi pergerakan penumpang pada waktu sibuk, dengan memperhitungkan peningkatan penumpang dimasa mendatang, sehingga didapat kesesuaian antara kapasitas terminal penumpang dengan luas terminal penumpang saat ini. Hasil penelitian untuk terminal penumpang keberangkatan, luas aktual terminal mampu menampung sirkulasi pergerakan penumpang pada waktu sibuk yaitu sebesar 220 penumpang, sementara terminal kedatangan kurang mampu untuk menampung sirkulasi pergerakan penumpang kedatangan pada waktu sibuknya yaitu sebesar 232 penumpang. Untuk LOS sendiri berdasarkan standar IATA mendapatkan hasil yang memenuhi standar.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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