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
Dynamic brittle fragmentation is typically described using analytical and computational approaches for tensile stress‐states. However, most fragmentation applications (e.g., impact, blast) involve very large initial compressive stresses and deformations. In this study, the compressive fragmentation of brittle materials is investigated experimentally across a range of materials: silicon carbide, boron carbide, spinel, basalt and a stony meteorite. Analysis of our experimental results suggests that there exists two different regimes in the fragment size distributions, based on two brittle fragmentation mechanisms. The first is a mechanism that produces larger fragments and is associated with the structural failure of the sample being tested. This mechanism is influenced by the loading conditions (rate, stress state) and sample geometry. The second fragmentation mechanism produces comparatively smaller fragments and arises from the coalescence of fractures initiating and coalescence between defects in regions of large stresses and contact forces (e.g., between two fractured surfaces from the larger fragments). A framework is developed for comparing experimental compressive fragmentation results with tensile fragmentation theories. The compressive experimental results are shown to be adequately described by the theories using the new framework.
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 | 0.000 |
| 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 it