Classification of Moving Ground Targets Using Measurement from Accelerometer on Road Surface
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 this study, an algorithm that can classify human and car has been developed by using vibration signals obtained from a three-axis accelerometer sensor station placed on three different floors. Data were collected over soil, asphalt and concrete ground. As classifiers, k-Nearest Neighbor classifier (k-NN) and Support Vector Machine (SVM) classifiers are used. Using classifiers alone limits classification performance. A two-stage classifier model has been proposed to improve the classification performance. The classifier model, which is proposed in two stages, detects the presence of motion in the first stage. In the second stage, it performs the classification of moving targets. As a result of the experimental studies, it has been shown that the proposed two-stage classifier model improves the performance in solving the problem.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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