Beatfrost Clothing Enterprise / Muhammad Amirul Razin Roslan ... [et al.]
Bibliographic record
Abstract
BEA TFROST ENTERPRISE is a company which focuses on clothing production in Malaysia. Since here are so many local brand exist in Malaysia. We want to make the name of Malaysia become popular in becoming the new trendy and branded clothes. Differences in size, colours and type of fabric make this clothes looks interesting and suitable to all consumers. The affordable price and the comfortable gives an opportunity to this clothes to be one of marketable product a people love simple things and affordable. Moreover, the simple design and high quality of fabric give a lot of benefits to our company and we are highly confident that our market can be easily developed and spread in Malaysia. To make it more attractive and interesting, we decided to make some innovation by implying our creativity and modern element in this clothes. Nowadays, various type of clothes is one of the important things in any products making so we not only producing a shirt but we are producing eat shirt, cap, bags, long sleeves shirt, quarter sleeves shirt and sleeveless shirt. So, the customers n have various choice. We have done some research that Malaysia is one of developed country advanced in fashion and most of the Malaysians love to wear local brand shirt nowadays.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.161 | 0.058 |
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".