Artificial Neural Network Calibration of Wide Range of Motion Biaxial Inclinometers
Bibliographic record
Abstract
Effective calibration of sensors with fine-scale irregularities in their residual space requires large and dense calibration datasets. For the case of biaxial electrolytic inclinometers, these irregularities are not evenly distributed and concentrate in small regions that drive data resolution requirements. Using evenly spaced sampling for look up tables results in less irregular regions being over-sampled, burdening the calibration process. Artificial neural networks have the capability to optimally distribute a limited number of trainable parameters to minimize the residuals. This can have the benefit of reducing data collection requirements as well as reducing memory requirements. In this paper, we compare the residual model accuracy of a neural networks and look up tables for biaxial inclinometers with temperature variability. We control for neural network size by equating trainable parameters to lookup table data and control for data acquisition by the number of sample points. To avoid biasing the neural network, we introduce random perturbation to otherwise uniform data sampling locations. For temperature dependent validation, we found that the neural network reduced the difference in performance between the orthogonal measurement channels by 99% as compared to a look up table.
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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".