Designing a Public API-Based Order Delivery Service System for the Food and Beverage Industry
Bibliographic record
Abstract
The food and beverage (F&B) industry aims to satisfy customers' needs by providing food and beverages while continuously developing creative and innovative steps to stay competitive. However, the COVID-19 pandemic has had a detrimental impact on the industry's survival, with a reduction of more than one-third of their daily income. One reason for this decline is the decreased interest of customers to dine in restaurants. To mitigate further losses, the F&B industry must innovate its services to meet the needs of customers who prefer to enjoy food at home or work. Delivery service is a critical aspect that must be developed, and adopting the appropriate information technology (IT) for service delivery automation can provide an economical solution and added value to the F&B industry. This study proposes using public APIs to support and enhance the F&B industry's delivery service system. Specifically, the WhatsApp API is selected to automatically notify drivers to process deliveries. This paper presents the design of a public API-based order delivery service system for the F&B industry that can be integrated with existing systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".