Robust QLM-SCFTK matrix approach applied to a biological population model of fractional order considering the carrying capacity
Bibliographic record
Abstract
Two effective and accurate matrix collocation techniques based on novel generalized shifted Chebyshev functions of the third kind (GSCFTK) are presented to examine the approximate solutions of a fractional-order population model considering the impact of carrying capacity. The fractional operator in the Liouville-Caputo sense is considered. The first direct technique is called the GSCFTK matrix procedure whereas the second one relied on the quasilinearization method and the GSCFTK matrix collocation strategy is called QLM-GSCFTK. In both approaches, the nonlinear population model is transformed into an algebraic system of equations. The resulting matrix equation is nonlinear in the first approach while is linear in the latter one, which is more efficient than the direct matrix method. The convergence analysis of the new generalized bases is established. To testify the robustness and effectiveness of the presented techniques, various numerical simulations are performed using diverse fractional orders. The results prove the applicability of the presented techniques of providing accurate results in comparison to other available numerical models and can be extended to other similar biological problems. Results also show that the numerical schemes are robust.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".