Metrological characterization of 3D imaging systems: progress report on standards developments
Bibliographic record
Abstract
A significant issue for companies or organizations integrating non-contact three-dimensional (3D) imaging systems into their production pipeline is deciding in which technology to invest. Quality non-contact 3D imaging systems typically involve a significant investment when considering the cost of equipment, training, software, and maintenance contracts over the functional lifetime of a given system or systems notwithstanding the requirements of the global nature of manufacturing activities. Numerous methods have been published to “help” users navigate the many products and specifications claims about “quality”. Moreover, the “best” system for one application may not be ideally suited for another application. The lack of publically-available characterization methods from trusted sources for certain areas of 3D imaging make it difficult for a typical user to select a system based on information written on a specification sheet alone. An internationally-recognized standard is a vehicle that allows better communication between users and manufacturers. It is in this context that we present a progress report on standards developments to date in the diverse, but finite, world of non-contact 3D imaging systems from the nanometre to the 100 m range.
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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 | 0.039 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".