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Record W4205727129 · doi:10.31227/osf.io/ws8kb

ANALISIS KARAKTERISTIK KEBUTUHAN RUANG PARKIR

2019· preprint· id· W4205727129 on OpenAlexaff
Thaib Salim

Bibliographic record

Venuenot available
Typepreprint
Languageid
FieldEngineering
TopicUrban Transport Systems Analysis
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsHumanitiesPhysicsMathematicsArt

Abstract

fetched live from OpenAlex

Kegiatan sosial ekonomi di kota-kota besar dan menengah mengalami peningkatan yang cukup berarti, dalam beberapa tahun terakhir. Kota Sorong yang termasuk kota menengah, sejalan dengan meningkatnya kegiatan ekonomi, permintaan akan fasilitas yang menunjang kegiatan tersebut juga semakin besar. Tujuan dari penelitian ini adalah untuk mengetahui karakteristik dasar ruang parkir, meliputi akumulasi parkir,volume parkir dan durasi parkirSerta mengetahui rekomdasi Amdal Lalin dari Izin Mendirikan Bangunan (IMB) Fave Hotel Kota Sorong.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.002

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 head0.012
GPT teacher head0.204
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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