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Record W2931873119 · doi:10.26760/rekaracana.v4i2.94

Analisis Karakteristik Gelombang di Perairan Pulau Enggano, Bengkulu. (Hal. 94-103)

2018· article· id· W2931873119 on OpenAlexaff
Akbar Hadiraksa Usmaya, Yati Muliati

Bibliographic record

VenueRekaRacana Jurnal Teknil Sipil · 2018
Typearticle
Languageid
FieldEnvironmental Science
TopicMarine and Coastal Ecosystems
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsEnvironmental science

Abstract

fetched live from OpenAlex

ABSTRAKPulau Enggano merupakan pulau kecil terluar bagian Indonesia yang terletak di Samudra Hindia. Pulau ini memiliki beberapa potensi yang bisa meningkatkan ekonomi di daerah tersebut. Pemerintah sedang mengkaji berbagai sumber energi baru salah satunya adalah energi gelombang laut di perairan Pulau Enggano, maka pemerintah melakukan pengukuran gelombang laut di Perairan enggano. Alternatif lain untuk lain untuk mendapatkan informasi tinggi gelombang adalah memanfaatkan data altimetri dengan satelit. Data gelombang dari satelit perlu divalidasi dengan data hasil pengukuran di lapangan dengan metode statistik, sehingga dapat dirumuskan karakteristik gelombang. Faktor koreksi yang didapatkan dari validasi adalah sebesar 0,466. Faktor koreksi ini akan dikalikan dengan data gelombang altimetri dan menampilkan besaran tinggi gelombang di perairan Enggano.Kata kunci: validasi, tinggi gelombang, Perairan EngganoABSTRACTEnggano island are of small island of Indonesia located in the Indian Ocean. The island has some potentials that could generate the economy in the area. The Government is improve a non fosil energy by using wave energy of Enggano Island. The wave height is measured to determine of enggano. Altimetry data is another way to get wave height is utilizing satellite data with altimetri. Wave data from satellites needs to be validated with field measurements of results data with statistical methods, so it can be deduced the characteristics of waves. Correction factors obtained from validation is of 0.466. This correction factor will be multiplied by altimetri and wave data showing high wave magnitudes in the waters of Enggano.Keywords: validation, wave haight, Water’s of Enggano

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.007

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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2018
Admission routes1
Has abstractyes

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