Towards the development of standards and performance metrics for 3D imaging systems
Bibliographic record
Abstract
NIST and multiple industrial stakeholders are leading and supporting multiple efforts to develop standards for 3D imaging systems for manufacturing automation applications. Many manufacturers specify the performance of their sensors in non-standard ways and offer no method to verify those parameters independently. The standards are being developed under the auspices of the ASTM E57 committee on 3D Imaging Systems. They are meant to produce a) standards for measuring the performance of 3D imaging systems, b) standards for bin-picking vision systems, and c) guidelines for the selection of 3D imaging systems. This work presents the status of four work items.
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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 | 0.104 | 0.121 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".