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
We present a method for designing and fabricating MetaSilicones ---composite silicone rubbers that exhibit desired macroscopic mechanical properties. The underlying principle of our approach is to inject spherical inclusions of a liquid dopant material into a silicone matrix material. By varying the number, size, and locations of these inclusions as well as their material, a broad range of mechanical properties can be achieved. The technical core of our approach is formed by an optimization algorithm that, combining a simulation model based on extended finite elements (XFEM) and sensitivity analysis, computes inclusion distributions that lead to desired stiffness properties on the macroscopic level. We explore the design space of MetaSilicone on an extensive set of simulation experiments involving materials with optimized uni- and bi-directional stiffness, spatially-graded properties, as well as multi-material composites. We present validation through standard measurements on physical prototypes, which we fabricate on a modified filament-based 3D printer, thus combining the advantages of digital fabrication with the mechanical performance of silicone elastomers.
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