A Numerical Method for Solving the Mobile/Immobile Diffusion Equation with Non-Local Conditions
Bibliographic record
Abstract
The purpose of this work is to use a new numerical technique for solving the two-sided multi-dimensional variable order fractional mobile/immobile diffusion equation with non-local conditions (TSMDVOF-MIDENLCs) model using the variable time fractional derivative of Caputo, as well as an initial boundary value problem of modified treatment. We used the fractional variational iteration method (FVIM) to mix initial and boundary conditions, resulting in for each iteration, a new initial solution. Convergence, sufficient conditions (SC) for system convergence, and error estimation are discussed. Some examples are given to illustrate the applicability of the novel suggested method, demonstrating that the numerical solution matches the exact solution and that the error is zero. Furthermore, this algorithm is easy and inexpensive to implement, and it demonstrates efficiency and accuracy.
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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) | 0.000 | 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 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".