Perubahan Morfologi Sungai Lariang: Analisis Spasiotemporal dengan Pendekatan Penginderaan Jauh
Bibliographic record
Abstract
Sungai merupakan salah satu unsur alam yang memiliki peran penting dalam ekosistem, baik dari segi penyediaan air, pengairan pertanian, hingga pendukung biodiversitas. Morfologi sungai yang terus berubah perlu dipantau secara berkala untuk mengetahui dinamika perubahan dan dampaknya terhadap lingkungan. Penelitian ini bertujuan untuk mengidentifikasi dinamika perubahan morfologi Sungai Lariang bagian hilir berdasarkan erosi dan deposisi sungai selama periode 10 tahun. Metode yang digunakan adalah analisis spasiotemporal pola erosi dan deposisi sungai pada tahun 2013, 2018 dan 2023. Analisis dilakukan melalui interpretasi citra satelit Google Earth Pro yang kemudian didigitasi menggunakan perangkat lunak ArcGIS. Hasil penelitian menunjukkan bahwa terdapat dinamika yang signifikan pada meander sungai di beberapa titik, terutama pada segmen-segmen yang memiliki tikungan tajam. Pada periode 2013-2018, luasan erosi adalah seluas 971.298 m2, sedangkan luasan akresi adalah seluas 1.624.959 m2. Pada periode 2018-2023, luasan erosi adalah 644.619 m2, sedangkan luasan akresi adalah 981.088 m2. Dinamika erosi dan akresi yang tinggi menyebabkan pembelokan sungai dan pembentukan bentuklahan baru.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
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".