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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".