PENGEMBANGAN EKSPOR PRODUK KOMPONEN OTOMOTIF BERBAHAN BAKU KARET
Classification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".
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
The big potential of natural rubber as a raw material of rubber goods industry encourages the Government of Indonesia initiated as rubber goods manufacturers leading the world. Industrial rubber goods in question one is wood-fired industrial rubber automotive components. Results of the study indicate that products automotive components made of rubber from Indonesia have not been developed. This is reflected from (i) the market share of the world's only 0.23; (ii) competitiveness in export markets is still relatively low, (iii) the degree of diversification of products and markets are still relatively low, (iv) export is not sensitive to changes in market share, and (v) a low per capita exports. In addition, automotive components made of rubber from Indonesia had positive prospects on two things, namely, (i) there are 3 excellent products, 5 products a priority, and 3 potential products, and (ii) the performance of export products was generally both Indonesia cabaret venue in the country of Japan, Malaysia, Thailand, Viet nam, the United States and Canada. Indonesia's automotive components industry made of rubber has not got special attention from the Government through policy and its development.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.011 |
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