Propagation of an Unmodeled Additive Constant in Range Sensor Observations
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
The rangefinder offset, additive constant or zero error is the most basic systematic error affecting the accuracy range measurements. Recent investigations of different systems that feature range measurement have shown that minimally constrained self-calibration adjustments of observations corrupted by an unmodeled rangefinder offset yield near-linear patterns in the range-observation residuals. This paper explains the underlying mathematical cause of this phenomenon for the purpose of assisting systematic error model identification. As a result of the mathematical derivations a new improved (in terms of parameter correlation) method for estimating the rangefinder offset from the sum of residuals of a least-squares adjustment of biased range observations has been developed. The new method is successfully demonstrated on data collected with total stations and ultrawide band ranging radios over two one-dimensional baselines.
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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 | 0.001 | 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 | 0.001 |
| 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