Bibliographic record
Abstract
Various combat models such as deterministic Lanchester, stochastic Lanchester and the general renewal models of combat have been widely used in for the analytical purpose to predict and to simulate the mutual attrition between two opponents[1]. One problem of those analytical solutions is that the methods of exhaustive enumeration, which have strong exponential computation time. Therefore, in practice, they are used for only small-to-moderate-size. The time complexity makes it practically impossible to use them for battles beyond 4 on 4[2]. Investigating the assumptions of the general-renewal model[3] for an army battle, one key requirement is that all combatants choose an opponent and fire independently, the authors consider the suitability of using a parallel approach. This paper discusses the possibility to use a parallel analytical solution of stochastic combat. The discussion is divided into three sections. Firstly, a review of analytical solutions of combat model is given. Secondly, our algorithm of a parallel analytical solution of stochastic combat is illustrated. Thirdly, conclusions are drawn and future directions are highlighted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 0.001 |
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".