Karaktersitik Pola Arus Laut di Perairan Selat Sunda Periode Tahun 2013 – 2023
Bibliographic record
Abstract
Penelitian ini mengkaji karakteristik pola arus permukaan laut di Perairan Selat Sunda menggunakan data arus model numerik dari Marine Copernicus selama periode 2013 – 2023. Analisis dilakukan untuk mengidentifikasi arah dan kecepatan arus pada periode musiman, yaitu musim barat (Desember – Januari – Februari), musim peralihan I (Maret – April – Mei), musim timur (Juni – Juli – Agustus), dan musim peralihan II (September – Oktober – November). Data arus model Marine Copernicus diverifikasi terhadap data pengamatan High Frequency (HF) Radar menggunakan metode statistik koefisien korelasi Pearson (r) dan Root Mean Square Error (RMSE), menghasilkan nilai r sebesar 0.77 dan RMSE 0.26 m/s, menunjukkan tingkat akurasi yang baik. Hasil analisis menunjukkan bahwa pada musim barat, arus dominan menuju ke timur laut dengan kecepatan rata-rata 0.5 – 1.0 m/s dan maksimum 1.5 – 2.0 m/s. Pada musim peralihan I, arus menuju ke timur laut dengan kecepatan rata – rata 0.75 – 1.25 m/s dan maksimum 1.5 – 2.0 m/s. Pada musim timur, arus dominan menuju ke barat daya dengan kecepatan rata – rata 1.25 – 1.75 m/s dan maksimum 1.5 – 2.0 m/s. Pada musim peralihan II, arus menunjukkan arah tidak beraturan menuju barat dan timur, dengan kecepatan rata – rata 1.0–1.5 m/s dan maksimum 1.5 – 2.0 m/s. Pola arus ini dipengaruhi oleh angin monsoon, topografi dasar laut, dan interaksi pesisir, yang berdampak pada navigasi, perikanan, dan ekosistem laut.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.013 |
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; both teacher heads agree on what is shown here.
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".