Topological characterizations of crystal cubic carbon structures
Bibliographic record
Abstract
Graph theory (GT) serves as a mathematical foundation that helps us to manipulate, develop, analyze, and comprehend the chemical networks or structures and their characteristics. Molecular graph is a graph made up of vertices (atoms) and edges (chemical bonds between atoms). Chemical GT applied geometrical and combinatorial GT to model the important chemical structures of chemistry. Chemical GT has a wide range of uses in the study of chemical structures. The examination and manipulation of chemical structural information is made feasible by utilizing the numerical graph invariants. A graph invariant or a topological index (TI) is a numerical measure of a chemical compound that is capable of describing the properties of chemical compounds, such as melting point, freezing point, density, pressure, tension, and temperature. In this article, we compute connection-based Zagreb indices (CBZIs), namely first CBZI, second CBZI, modified first CBZI, modified second CBZI, and modified third CBZI for one of the significant types of molecular structure named as crystal cubic carbon structure, which is the most important allotrope of carbon atom. Moreover, to examine the superiority and authenticity of our computed TIs, we compare the calculated values of these ZIs for crystal cubic carbon structure with each other. This comparison enables us to check which CBZI is more authentic and superior to predict the physical and chemical properties of crystal cubic carbon structure.
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) | 0.000 | 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) | 0.002 | 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".