UJI KANDUNGAN LOGAM BERAT SENG (Zn) PADA AIR \nIRIGASI, TANAH DAN SAYURAN KANGKUNG (Ipomoea \nreptans Poir.) DI KAWASAN INDUSTRI KECAMATAN \nMARGAASIH KABUPATEN BANDUNG
Bibliographic record
Abstract
Kecamatan Margaasih merupakan salah satu kawasan industri di Kabupaten \nBandung. Perkembangan industri selain memberikan lapangan pekerjaan juga \nmeningkatkan jumlah limbah yang dihasilkan. Penelitian ini bertujuan untuk \nmendapatkan data dan informasi mengetahui kandungan logam berat seng (Zn) \nyang terdapat pada air irigasi, tanah dan sayuran kangkung di kawasan industri \nKecamatan Margaasih Kabupaten Bandung. Penelitian ini dilakukan pada bulan \nMei 2022. Metode yang digunakan adalah analisis deskriptif dengan teknik \npengambilan sampel menggunakan metode purposive sampling pada tiga plot \npengamatan dan di analisis menggunakan Atomic Absorption Spectrophotometry \n(AAS) di Laboratorium Sentral Universitas Padjajaran. Hasil penelitian \nmenunjukkan kandungan logam berat seng (Zn) pada air irigasi sebesar 0,0452 \nmg/L berada dibawah baku mutu yang ditetapkan oleh Peraturan Pemerintah \nRepublik Indonesia No. 22 Tahun 2021. Pada tanah sebesar 171,4225 mg/Kg \nberada diatas baku mutu yang ditetapkan oleh Ministry of State for Population \nand Environment of Indonesia and Dalhousie University, Canada (1992). Pada \nsayuran kangkung sebesar 12,4450 mg/Kg berada dibawah baku mutu yang \nditetapkan oleh Dit Jend POM No 03725/B/SKVII/89. Keadaan lingkungan yang \ndiukur pada saat penelitian yakni suhu udara kisaran 28,77 \nC, intensitas cahaya \nkisaran 23.867,77 Lux, sedangkan pH pada tanah kisaran 6,4. Berdasarkan \npenelitian yang dilakukan menunjukkan status baku mutu sayuran kangkung di \nkawasan industri Kecamatan Margaasih Kabupaten Bandung tercemar ringan \nlogam seng (Zn). \n \nKata Kunci : Logam berat, seng (Zn), air irigasi, tanah dan sayur kangkung
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.020 |
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".