Analisis Karakteristik Gelombang di Perairan Pulau Enggano, Bengkulu. (Hal. 94-103)
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".