Simulation Evaluation Algorithms of LSRP and DVRP in Bank Network
Bibliographic record
Abstract
Because the computational complexity of high usability analysis is too high when selecting the routing protocol in the bank network plan, a simple measurement method of high usability for banks is proposed to solve this problem in the article. First, establish a simulation environment which is close to the real network of banks to offer the measurement environment and data. Second, based on the theoretical comparison of LSRP and DVRP, establish the simple simulation measurement algorithm by Shannon’s information entropy theory. Finally, evaluate the degree of high usability of LSRP and DVRP in the bank network by this algorithm. The result of simulation shows that (1) the simulation is close to the periodic rule of the statistical data group in real environment, (2) the deviation ratio is less than 0.1, and (3) the covariance is unequal to 0. And the result indicates that the simulation is connected with the real environment and both are very close, and the simulation environment can offer effective data. The computation result of the simple simulation measurement algorithm shows that the time cost differences of LSRP and DVRP exist in the period of fault recovering, which indicates the simple measurement algorithm is effective.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".