Monitoring of food spoilage with electronic nose: potential applications for smart homes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In ambient-assisted living environments, advanced sensors are used to detect potential problems that may affect the occupant. For a range of unsafe living conditions, characteristic odours arise that can provide early warning of a problem in the dwelling. In this paper, we investigate the concept of smell monitoring in the smart home environment, with particular attention paid to food spoilage. Using a commercially available electronic nose (e-nose) based on a metal-oxide sensor array, the odours associated with five common foods were captured over a seven day period. All foods were readily discriminated at the beginning of the measurement period. However, as the food spoiled, the odour profiles changed significantly. In several cases, the changes for a given food exhibited a clear trajectory in the PCA space. This preliminary work suggests that e-nose technology is a promising candidate for incorporation in the smart home. For widespread adoption, however, future e-nose development must continue to improve current shortcomings such as instability, user intervention, and high cost.
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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 | 0.000 | 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 it