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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 0.002 |
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".