A Fuzzy Markup Language-Based Approach for a Quality of Location Inference as An Environmental Health Awareness
Bibliographic record
Abstract
In a recent study conducted over a two-month period, the authors have concluded that there exists a correlation between two environmental parameters, namely air quality and noise. The correlation efficiency results obtained were quantitative in nature, and thus cannot be intuitively interpreted by humans. In this article, the authors propose a Fuzzy-markup language (FML) model that aims to translate the degree of correlation that may exist among IoT environmental parameters into a linguistic set of indicators. For that purpose, the authors have developed a Fuzzy-Inference System (FIS) that infers the quality of location status according to people's surroundings and provide health-aware notifications accordingly. The initial FIS results show the significance of timeframe that should be considered according to each sensory data source. In other words, the correlation degree of the two sensory set is clearly affected by time frame variations.
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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 | 0.002 | 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".