Adaptable Context-Aware Cooking-Safe System
Why this work is in the frame
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Bibliographic record
Abstract
Kitchen safety is a highly important concern for daily living activities. Cooking, usually, is accompanied with several risks particularly for elderly people, due to aging associated impairments. Therefore, cooking-safe environment is required to enhance safety. In this paper, we present our cooking-safe smart oven system which manages the detection of risk situations and determines their severity levels according to the contextual information around oven. The context is gathered via sensors deployed in the kitchen environment. The cooking-safe system is composed of sensor nodes, actuators, microcontroller, and a computing unit. Cooking related risks are managed by the fuzzy logic based reasoning engine of the cooking-safe system. We also present in details our risk prevention algorithms which constitute the basic concepts of the reasoning engine. We discuss the system evaluation in real-world environment, and the interventions via interactive interfaces with users.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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 it