THE IMPACT OF THE DISTRIBUTION OF OBSERVATIONS ON TERRESTRIAL LASER SCANNER SELF-CALIBRATION QUALITY
Bibliographic record
Abstract
Abstract. Terrestrial laser scanning (TLS) instruments find routine use for a range of precision engineering measurement applications. Depending on the accuracy requirements for a specific project, the instrument may require self-calibration to determine systematic error model terms. One of the goals of first-order network design for self-calibration is to acquire observations throughout the full instrumental field-of-view. Experience calibrating TLS instruments has demonstrated that while this goal can be achieved for horizontal deflection angle observations, it is seldom realized for the vertical angle observations. This paper presents results from a preliminary investigation into the impact of the distribution of vertical angle observations on the estimation of two critical systematic error parameters in TLS instruments: the collimation axis error and the trunnion axis error. First, a model to characterize the empirical observation distributions is developed. The model is a function of a single shape parameter that quantifies observation dispersion. Then, a means to estimate the impact of the distribution on the parameter estimation is developed. Results from six real datasets show the distribution model characterizes the overall general trend of the observations. Simulated results show the relative independence of the collimation axis error and the strong dependence of the trunnion axis error on the shape parameter.
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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 | 0.011 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".