APLIKASI TEKNOLOGI PENGOLAHAN AIR ASIN MENGGUNAKAN MEMBRAN REVERSE OSMOSIS DI PULAU BARRANG CADDI, MAKASSAR
Bibliographic record
Abstract
Pulau Barrang Caddi memiliki luas wilayah 4 ha dengan jarak 11 km dari Kota Makassar dan merupakan pulau yang padat penduduknya yang berjumlah 1263 jiwa. Untukmendapatkan air bersih, masyarakat umumnya menggali sumur dangkal, namun airnyaberubah menjadi asin pada musim kemarau Pada pemukiman yang padat, kualitas airsumurnya menurun dari tahun ke tahun. Air bersih merupakan barang langka di pulaupulaukecil, terutama pada lokasi yang penduduknya padat. Kualitas air tanah dangkalsemakin menurun disebabkan oleh intrusi air laut, dimana air menjadi asin karenatingginya kadar garam. Untuk menyajikan air minum yang sehat harus dibeli denganharga yang mahal dan hanya ada di Kota Makassar. Untuk mengatasi masalah tersebut maka diperlukan pengolahan air dengan teknologi reverse osmosis yang dapatmengolah air asin menjadi air tawar. Kegiatan ini meliputi survei, desain, pretreatment,pengolahan lanjut dan pasca produksi. Teknologi Sea Water Reverse Osmosis (SWRO)pada Unit Arsinum yang diaplikasikan di P. Barrang Caddi ini menghasilkan air produkolahan yang layak minum dan sesuai dengan baku mutu. Selain itu, dilakukan pulaperhitungan biaya energi hasil Unit Arsinum tersebut untuk memenuhi biaya operatordan perawatan. Kata Kunci: teknologi, pengolahan, air asin, reverse osmosis, air minum
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".