Characterization of Ultra-High Temperature and Polymorphic Ceramics
Bibliographic record
Abstract
Material properties are highly dependent on the hierarchy of length and temporal scales experienced by the structure, where atomic levels can influence characterized structures through certain features pertaining to atomic species, their orientations and crystal types. Analyzing such features with the underlying atomic group symmetry is necessary to characterize hierarchical properties that are influenced from external stimuli, load rates, and sample sizes. In hierarchical multiscale models, one can obtain certain properties at larger scales by analyzing a smaller scale for material properties and/or mechanics and bridge such properties to the higher scale in a sequential fashion [1]. It depends on the nature of the material on how small a level one can go without sacrificing details to understand themacroscopic behavior. For example, nanocomposites reinforced with carbon nanotubes must be analyzed in an atomic scale to develop a hierarchical relationship with respect to the nanotube’s orientation, structure and size for dynamic, mechanical and structural composite properties [1]. Stimuli responsive behavior that alters the crystalline structure may require an analysis of crystalline features. Two common methods of characterizing crystalline structures are centrosymmetry parameter (CSP) [2, 3] and common neighbor analysis (CNA) [4, 5]. Despite being well defined for characterization of local deformation and distinctive atomic arrangements through atomic neighbor topologies, such methods are reported to work for simplified structures (such as face centered cube [FCC], body centered cube [BCC] and hexagonal close packed [HCP] structures) that do not distinguish the atomic space group symmetries. Hence, more advanced crystal characterization methods are desirable when considering structures with non-centrosymmetric, multiple atomic species and multiple space group arrangements.
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.
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) | 0.001 | 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) | 0.010 | 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".