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Record W4416920926 · doi:10.5206/mase/22837

A new variant of the Sombor matrix: bounds on spectral radius and energy with applications to QSPR analysis of COVID-19 drugs

2025· article· en· W4416920926 on OpenAlexvenueno aff
G. Veeresha, Pannagadatta K. Shivaswamy

Bibliographic record

VenueMathematics in Applied Sciences and Engineering · 2025
Typearticle
Languageen
FieldMathematics
TopicGraph theory and applications
Canadian institutionsnot available
Fundersnot available
KeywordsEigenvalues and eigenvectorsSpectral radiusGraphSimple (philosophy)Energy (signal processing)Degree (music)Matrix (chemical analysis)Zero (linguistics)Linear regression

Abstract

fetched live from OpenAlex

In this paper, we introduce a novel variant of the Sombor matrix, denoted as $\mathcal{NS}o(\zeta)$, for a simple graph $\zeta(\mathcal{V}, \mathcal{E})$. The matrix is defined such that for $i \neq j$, the $(i,j)$-entry is given by $\sqrt{d_{i}^{2}+d_{j}^{2}}$, where $d_i$ represents the degree of the $i^{\text{th}}$ vertex, and zero otherwise. Let $\eta_1 \geq \eta_2 \geq \cdots \geq \eta_{\rho}$ denote the eigenvalues of $\mathcal{NS}o(\zeta)$, with $\eta_1$ being the spectral radius. The Sombor energy $E_{\mathcal{NS}o}(\zeta)$ is defined as the sum of the absolute values of these eigenvalues. We derive upper and lower bounds for both $\eta_1$ and $E_{\mathcal{NS}o}(\zeta)$ in terms of the first Zagreb index ($M_1(\zeta)$). As an application, we perform a Quantitative Structure-Property Relationship (QSPR) analysis using a dataset of drugs employed in the treatment of COVID-19 patients. We construct linear, quadratic, and cubic regression models to explore the relationship between the physicochemical properties of these drugs and their corresponding $E_{\mathcal{NS}o}(\zeta)$ values, with the models visualized through graphical representations.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.649
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.284
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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