Prediction of Food Preparation Time for Smart City
Bibliographic record
Abstract
With advancements in digital technologies, very large volumes of big data can be generated and collected from a wide variety of rich data sources in various real-life applications, including those from many of our daily routines. Embedded in these big data are useful information and valuable knowledge. Analyzing and mining these big data helps discover useful information and valuable knowledge, which may enhance performance and well-being, as well as reduce costs and resource consumption (e.g., time), of citizens. Quality of food could enhance our lifestyle, and thus our health. While home cooking is considered healthy, occasion take-out may save time. When ordering take-out, it is challenging for the customers or delivery persons to predict the right time for picking up the order. In this paper, we present a smart city system that makes good use of big data mining to predict food preparation time. Evaluation results show that our system is effective and practical in predicting food preparation time, and thus leading to benefits towards building a smart city.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".