PENGOLAHAN LIMBAH CAIR LABORATORIUM DENGAN ADSORPSI SERTA PRETREATMENT NETRALISASI DAN KOAGULASI
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
Limbah cair laboratorium Teknik Lingkungan UNIPA Surabaya belum memenuhi baku mutu Peraturan Menteri Lingkungan Hidup Nomor 5 Tahun 2014, sehingga perlu diolah supaya tidak mencemari lingkungan. Penelitian ini bertujuan mengkaji pengaruh dosis koagulan Poly Alum Chloride (PAC) terhadap penurunan Pb, Cr, dan TDS, mengkaji kualitas air limbah setelah dinetralisasi, dikoagulasi dan diadsobsi terutama untuk parameter Pb, Cr, TDS, dan pH. Variabel penelitian ini adalah dosis PAC yaitu 150 mg/L, 225 mg/L dan 300 mg/L. Penelitian dilakukan dalam skala laboratorium dengan sistem kontinyu dengan aliran down flow. Media adsorpsi yang digunakan ijuk, sabut kelapa, karbon aktif ampas tebu dan zeolit yang disusun bertingkat dalam reaktor dari pipa PVC. Proses adsorpsi dilakukan selama 2 jam dan pengambilan sampel setiap 15 menit. Hasil dari penelitian ini menunjukan bahwa PAC 300 mg/L menghasilkan efisiensi penurunan tertinggi, yaitu TDS 13,7% Cr 97%, Pb 93,5%, dan kualitas limbah setelah dinetralisasi, dikoagulasi dan diadsorpsi pada menit ke-15 mempunyai kadar TDS 1.810 ppm, Cr total 0,36 ppm, Pb 0,66 ppm sehingga air limbah sudah memenuhi baku mutu sesuai dengan Peraturan Menteri Lingkungan Hidup No. 5 Tahun 2014 sedangkan pH sebesar 5,42 belum memenuhi baku mutu.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it