Application of Plated Rubber System (PRESS) for Rubber Identification
Bibliographic record
Abstract
Rubber prices are influenced by several factors including supply and demand especially from countries who are major rubber producers and consumers as well the growth rate of the world economy. High rubber prices increase the incidence of rubber theft, thus forcing the owners to build hut and camp in their rubber estates to prevent such occurrence. Rubber theft becomes prevalent not only at night but also in the broad daylight as the commodity price increases. From the survey, it has been noted that more than 10 incidents of theft have been reported within 15 days. Plated Rubber System (PRESS) was developed to imprint an identification entity on rubber that leads to identifying the ownership. With the invention of this system, the stolen number of rubber can be reduced because each rubber smallholders has their personal serial identification plate. The main objective PRESS is to establish an identification of rubber, helping to reduce the theft of rubber at farm level as well as to curb the sale of stolen rubber. In addition, it can be used to regulate the quality of rubber produced by smallholders and developing a database profile of rubber smallholders. The finding shows that the respondent agreed that the innovation of PRESS may help them to reduce the prevalence of rubber theft and increase the good practice of plantation management that leads to greater efficiency and productivity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".