Normalized Semi-Covariance Co-Efficiency Analysis of Spike Proteins from SARS-CoV-2 variant Omicron and Other Coronaviruses for their Infectivity and Virulence
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Spectrum-based Mass-Charge modeling is increasingly used in biological analysis. To explain statistical phenomenon with positive and negative fluctuations of amino acid charges in spike protein sequences from Omicron and other coronaviruses, we propose calculation-based Mass-Charge modeling, a normalized derivation algorithm with exact Excel and MATLAB tool involving separate quadrant extension to normalized covariance, which is still compatible with Pearson covariance co-efficiency. The number of amino acids, molecular weight, isoelectric point, amino acid composition, charged residues, mass-charge ratio, hydropathicity of the proteins were taken into consideration in the analyses, and the relative peak and dip of the average with spike protein sequences based on hydrophobic mass to isoelectric charges of amino acids were also examined. The analyses with the algorithm provide more clear insights leading to revealing underline evolving trends of the viral proteins. Spike proteins from SARS-CoV-2 variants, seasonal and murine coronaviruses were taken as representative examples in this study. The analyses demonstrate that the Mass-Charge covariance co-efficiency can distinguish subtle differences between biological properties of spike proteins and correlate well with viral infectivity and virulence.
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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 | 0.001 | 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 | 0.001 |
| 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