Visualising complex morphology of fatigue cracks in voxel based 3D datasets
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
Fatigue cracks are usually characterised by surface sensitive techniques after specimen failure. High resolution micro computed tomography (μCT) based on synchrotron radiation allows the non-destructive visualisation of crack morphology and evaluation of fatigue crack formation/propagation before specimen failure. The visualisation of the complex fracture morphology with characteristic features out of the acquired set of slices is, however, challenging. To obtain a reasonable estimate, two approaches are generally used: the determination of mass centre points in the hollow space and the minimum intensity search in parallel projections. The more sophisticated approach using the elastically deformable contour model, the physical analogy of a rubber band, termed snakes, gives rise to crack morphologies with much less artefacts. The approach was used in the present study for the characterisation of fatigue cracks in poly(methylmethacrylate) (PMMA) and a dental ceramic. The search for the appropriate snake parameters works much better for homogeneous materials, here PMMA, than for inhomogeneous materials, here a dental ceramic. For the ceramic, the regions where the snakes approach provided reasonable results were restricted. Combining μCT with sophisticated computer vision techniques enables the unique characterisation of cracks at the micrometre scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it