Laundrybar / Siti Nurbahiah Adenan
Bibliographic record
Abstract
LaundryBar is a local company which is focusing on the laundry service. In this company analysis, it is focusing on the machine that used for laundry equipment such as washing machine, dryer and ironers. The SWOT analysis has been conduct for this company where it has been investigate, identified and analysed along with their current problem and come out with solutions. The first problem of the product is extra labor in handling quarter. As we know, the uses of quarter is bit complicated because it need a more time for owner or operator of laundry bar to handle it. This is because the laundry washing machine still rely on the coin operated system. This system is give a more work to the owner because they need to always go to the bank for exchanging the quarter if the quarter is missing or not enough. Therefore, this product also involving to the electricity where it use a high consumption of electricity to make the washing work done quickly. This will give a burden to the owner because the bill or cost of the laundry business will become higher. Then, the product also do not contain a temperature guide for washing clothes. Aside from that, there are a few solution that has been analyse to improve the product. There are three solution has been taken to overcome this problem which is changing the coin operated mechanism to smart paying, apply eco mode to the washing machine and increase the selection for temperature setting. All of the solution are considered to make the company producing a way quality product that to be served to their customer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.179 | 0.066 |
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".