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Record W4252428567 · doi:10.31227/osf.io/t7pds

PENGARUH GEOMETRI JARINGAN TERHADAP KETELITIAN SURVEY GPS

2018· preprint· id· W4252428567 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
Typepreprint
Languageid
FieldComputer Science
TopicComputer Science and Engineering
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysics

Abstract

fetched live from OpenAlex

Kualitas dari koordinat titik-titik dalam suatu jaringan yang diperoleh dengan survey GPS secara umum akan tergantung pada empat faktor yaitu : ketelitian data yang digunakan, geometri pengamatan, strategi pengamatan yang digunakan, dan strategi pengolahan data yang diterapkan. Geometri pengamatan sendiri merupakan kombinasi dari geometri jaringan dan geometri satelit. Dalam makalah ini akan dibahas pengaruh dari faktor geometri jaringan terhadap kualitas koordinat yang diperoleh dari hitung perataan jaringan GPS. Dalam hal ini parameter dari geometri jaringan yang akan ditinjau pengaruhnya terhadap ketelitian survey GPS adalah : jumlah dan distribusi dari titik tetap (titik kontrol), jumlah baseline dalam satu loop, serta konektivitas titik (jumlah baseline yang terikat ke suatu titik). Pembahasan akan didasarkan pada hasil-hasil yang diperoleh dari pengolahan data jaringan GPS Orde-3 Badan Pertanahan Nasional (BPN) di daerah Purwodadi dan Wates. Makalah akan diakhiri dengan beberapa catatan penutup.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0090.011
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.002

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 head0.048
GPT teacher head0.257
Teacher spread0.209 · 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

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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