Kontribusi Industri Tekstil dalam Penggunaan Bahan Berbahaya dan Beracun Terhadap Rusaknya Sungai Citarum
Bibliographic record
Abstract
Indonesia merupakan Negara yang masuk dalam jajaran 10 besar pengeksporpakaian terbesar dunia dan pada tahun 2011 Indonesia merupakan negarapengekspor terbesar ke-11 di dunia. Indonesia adalah negara dengan ekonomiyang paling besar di Asia Tenggara, dan sektor tekstil menyumbang 8,9 persentotal ekspor Indonesia pada 2010. Tulisan ini akan melihat bagaimana kontribusisektor industri tekstil terhadap rusaknya Sungai Citarum. Metodologi penulisanini munggunakan pendekatan yuridis normatif yang diperkuat oleh kasus kegiatanindustri yang letaknya bersebelahan dengan Sungai Citarum. Sungai Citarummemiliki reputasi buruk sebagai sungai terkotor di dunia. Masalah kasat mataberupa sampah dan limbah domestik memang terlihat parah. Tetapi limbah daribahan berbahaya dan beracun yang digunakan dalam industri tekstil merupakansumber besar dari pencemaran dengan konsekuensi jangka panjang yang lebihserius, terutama di bagian hulu Sungai Citarum di mana terdapat 68 persen pabriktekstil.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".