Detecting Physical Defects: A Practical 2D-Study of Cracks and Holes
Bibliographic record
Abstract
Theoretical work on Qualitative Spatial Reasoning (QSR) is abundant, but the actual requirements of practical applications have been widely ignored. This paper discusses how ontolo-gies allow to compare different QSR formalisms with respect to definability of spatial concepts, which are taken from a real-world problem. We introduce the problem of detecting phys-ically defective parts (such as in manufacturing) and review which qualities are necessary for modeling these as QSR prob-lem. We show that – besides standard mereotopological con-cepts – a set of artifacts, especially cracks and holes, are of foremost importance in the domain of interest. However, most currently available region-based QSR approaches fail to distin-guish these. In the future, the proposed set of problem can be used to evaluate different QSR formalisms for their adequacy with respect to defining and distinguishing cracks and holes.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".