PENJERNIHAN AIR SUNGAI DENGAN PERLAKUAN KOAGULASI, FILTRASI, ABSORBSI, DAN PERTUKARAN ION
Bibliographic record
Abstract
Masyarakat di kawasan tepi sungai bagian pesisir sering mengalami krisis air bersih yang disebabkan oleh tingginya salinitas air tanah. Sebagai alternatif untuk mendapatkan air bersih, pada umumnya masyarakat menggunakan bahan baku air sungai yang keruh disaring dengan menggunakan kerikil dan pasir, namun hasilnya belum jernih. Cara mengatasinya adalah menggunakan teknologi tepat guna berupa pengolahan air dengan treatment koagulasi, filtrasi, absorbsi, dan pertukaran ion. Tujuan penerapan IPTEKS dalam program ini adalah ; mengatasi masalah kesulitan penjernihan air sungai agar menghasilkan air hasil olahan menjadi jernih. Metode yang digunakan adalah ; sosialisasi, pelatihan serta managemen produk tentang pengolahan air sungai menggunakan ”Water Treatment” untuk menghasilkan air bersih yang layak dikonsumsi. Teknologi yang diterapkan adalah sebagai berikut ; Bahan baku air sungai sebelum masuk bak pengolah dilakukan pretreatment dengan koagulan Poly Aluminium Chloride (PAC). Pada bak pengolah (I) dilakukan filtrasi, bak pengolah (II) treatment zeolit dan MGS, bak pengolah (III) berisi pasir silika dan resin sintetis. Air sungai yang keruh jika dilakukan pengolahan (treatment) menggunakan koagulan Poly Aluminium Chloride (PAC) dilanjutkan dengan filtrasi oleh filter spoon, kemudian absorben zeolit dan MGS, filter pasir silika dan diakhiri menggunakan resin sintetis kation dan resin sintetis anion dapat menghasilkan air yang jernih. Kata kunci ; Absorbsi, Filtrasi, Koagulasi, Pertukaran Ion
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".