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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".