Kontribusi Industri Tekstil dalam Penggunaan Bahan Berbahaya dan Beracun Terhadap Rusaknya Sungai Citarum
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
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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