Kajian Dampak Proses Pengolahan Air di IPA Siwalanpanji Terhadap Lingkungan dengan Menggunakan Metode Life Cycle Assessment (LCA)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Proses pengolahan air minum secara konvensional dapat menyebabkan dampak lingkungan akibat konsumsi energi dan pemakaian bahan kimia. Penelitian ini mengidentifikasi dampak pencemaran yang dihasilkan dari proses pengolahan air di IPA Siwalanpanji menggunakan <em>life cycle assessment</em>. <em>Life Cycle Assessment</em> (LCA) merupakan metode untuk menganalisis dampak suatu produk terhadap lingkungan sepanjang siklus hidupnya. Siklus hidup dari suatu produk terdiri dari ekstraksi bahan baku, proses produksi hingga proses pembuangan akhir. Dari hasil analisis LCA, menggunakan <em>software</em> Simapro 7.33 dampak pencemaran yang terjadi berupa pencemaran udara yang disebabkan oleh penggunaan klorin, <em>polyaluminium chloride</em> (PAC) dan konsumsi listrik. Dampak pencemaran terbesar terjadi pada penggunaan listrik dalam pemakaian satu hari yaitu menyebabkan <em>respiratory inorganics</em> sebesar 0,748 kg PM<sub>2.5</sub>, <em>ozone layer depletion</em> sebesar 0,000295 kg CFC-11 dan <em>global warming</em> sebesar 1000 kg CO<sub>2</sub>. Solusi untuk mengurangi dampak lingkungan yang dapat dilakukan instalasi pengolahan air adalah dengan cara peningkatan efesiensi peralatan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it