INTELLIGENTE SALZFABRIK SELF INTEGRATED PHARMACEUTICAL RAW MATERIALS INDUSTRY IN INDONESIA
Bibliographic record
Abstract
ABSTRACT Currently, Indonesia's pharmaceutical industry is still heavily dependent on imported raw materials, almost 95% of the needed medicine raw materials (BBO) still have to be imported from abroad. Based on data from the Directorate General of Foreign Trade, Ministry of Trade Republic of Indonesia, it was showed that the pharmaceutical salt import in 2013 reached 3,152 tons and all of them needed to fulfill domestic needs. This study used literary study method by collecting data or information in accordance with the topic. Geographically Indonesia consists of islands large and small number of approximately 17,504 islands. Three quarters of its territory is the ocean (5.9 million km2), with a 95,161 km long coastline, the second longest in the world after Canada. This makes Indonesia the world's largest archipelago in the world. This written idea was created as a solution to the problem of dependence on medicine raw materials import in the pharmaceutical industry of Indonesia. The solutions presented are Intelligente Salzfabrik: The Concept of Self-Integrated Pharmaceutical Raw Materials Industry which is Energy Independence and High Accessibility on Coastal with Sea Toll and Power Flow to Achieve An Imported Medicine Raw Materials Independence in Indonesia. This development will be implemented in close proximity to coastal areas near the the sea so that it can simplify both cost and transportation required for the distribution of salt produced.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".