Bibliographic record
Abstract
According to the definition of molecular connectivity and the definition of a hyper-Wiener index, a novel set of hyper-Wiener indexes (Rn, mRn) are defined and are named the extended hyper-Wiener indexes. Where n = 1, 2, 3, 4,... represents the type of subgraph units and is the number of endmost atoms of the subgraph unit, m is the number of atoms of the subgraph unit. Here n = 1 means the subgraph unit is an atom, n = 2 means the subgraph units are straight-line combinations of m atoms (m = 2, 3, 4, 5, 6,...), and n = 3 means the subgraph units are Y types of combinations of m atoms (m = 4, 5, 6, 7, 8,...), and so on. The potential usefulness of the extended hyper-Wiener index in QSAR and (or) QSPR is evaluated by its correlation with a number of C3C8 alkanes and by a favorable comparison with models based on the molecular connectivity index and the overall Wiener index. To verify the robustness and the predictive ability of the models, a cross-validation procedure, leave-one-out, and a random test were also performed. The results show that the extended hyper-Wiener indexes examined demonstrate a good potential for QSAR and QSPR studies. Considerably better statistics are obtained when extending the hyper-Wiener index to the extended hyper-Wiener index. The extended hyper-Wiener indexes provided statistical results as good as the molecular connectivity indexes and the overall Wiener index in all models, and the standard deviations provided by these three sets of indexes are rather close. Moreover, this method may provide a better way to apply the Wiener number and the hyper-Wiener index to the system of unsaturated hydrocarbons and organic compounds, including heteroatoms, according to the method of the molecular connectivity index. This can extend the usefulness of the Wiener number and hyper-Wiener index and can make them a kind of widely used topological index in practice.Key words: hyper-Wiener index (R), extended hyper-Wiener index, molecular connectivity index.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".