Teras Samudera Enterprise / Ahmad Tamrin... [et al.]
Bibliographic record
Abstract
Food industries are very much on demand. It is considered as an important thing in everybody's life. Processed food sales in 2004 alone are approximately US$3.2 trillion. In US, consumers spend approximately US$1 trillion annually on food which is 10% of its GDP. There are over 16.5 million people employed in food industry worldwide. Realizing the significance of the fact, we aim to establish a seaweed supplying company named Teras Samudera Enterprise. Seaweeds are popular in Japan, China, Korea, Taiwan, Thailand, Cambodia, Vietnam, Indonesia, Belize, Peru, Chile, Canada, Scandinavia, Ireland, Wales, Philippines, and Scotland. Seaweed are rich in calcium, magnesium and iodine where is the one of healthy foods. According to our research, demand for seaweed is increasing days to days. Plus, there are not many seaweed suppliers in Malaysia, especially here in Sabah. So, it is our aim to play a major part in this kind of product by specializing in seaweed with introducing different flavors to our product. We choose to establish a seaweed supplying company because there is a good future in it as it is able to expand and sustain profit. As a supplier, all we need to do is having the right place, which brings benefits to us. Our proposed company will be located at Tuaran. The location chosen is suitable for us because it has many retail stores around that will be our potential customers and users of our services. It is also close to the main road making it easier for distributing activities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.037 |
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".