UJI KANDUNGAN LOGAM BERAT TIMBAL (Pb) PADA AIR \nIRIGASI, TANAH DAN SAYURAN BAYAM DI KAWASAN \nINDUSTRI KECAMATAN MARGAASIH KABUPATEN \nBANDUNG
Bibliographic record
Abstract
Kecamatan Margaasih adalah salah satu kecamatan yang terdapat di Kabupaten \nBandung Jawa Barat yang terdapat banyak industri-industri dan lahan pertanian \nyang terindentifikasi kemungkinan adanya logam berat Timbal (Pb). Penelitian ini \nbertujuan untuk mengetahui logam berat Timbal (Pb) yang terdapat pada air irigasi, \ntanah dan sayuran bayam di Kawasan Industri Kecamatan Margaasih Kabupaten \nBandung. Pada tanggal 11 Mei 2022. Metode yang digunakan adalah deskriptif \ndengan pengambilan sampel secara purposive sampling pada tiga plot dengan \nmenggunakan instrumen analisis Atomic Absoption Spectrofotometri (AAS) di \nLaboratorium Sentral Universitas Padjadjaran. Hasil penelitian analisis data utama \nmenunjukkan kandungan logam berat Timbal (Pb) pada air irigasi sebesar 0,0132 \nmg/L masih berada dibawah baku mutu berdasarkan PP RI No.22 Tahun 2021; \nkandungan logam berat Timbal (Pb) pada tanah sebesar 13,64576 mg/Kg masih \nberada dibawah baku mutu berdasarkan Ministry of State for Popution and \nEnvironment of Indonesia, and Dalhousie University, Canada (1992); dan \nkandungan logam berat Timbal (Pb) pada sayuran bayam sebesar 0,2942 mg/Kg \nmasih berada dibawah baku mutu berdasarkan SNI No.7387 Tahun 2009. Faktor \nklimatik sebagai data penunjang dengan parameter yang diukur yaitu suhu udara \nberada pada kisaran 26 - 31˚C, intensitas cahaya berada pada kisaran 7070 – 51367 \nLux, dan pH tanah didapatkan 5,6. \nKata Kunci: Air Irigasi, Logam Berat, Tanah, Sayuran Bayam, Timbal (Pb)
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.014 |
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".