Удосконалення науково-методичного апарату визначення оптимальної стратегії вибору елементу угруповання для виконання завдання
Bibliographic record
Abstract
The object of this study is the process of determining the optimal strategy for choosing a certain element of the grouping to perform a certain task. The problem solved was the contradiction between the need to take into account various types of adverse conditions when determining the optimal strategy for assigning a certain type of forces and means for a certain task to the existing approach to maximizing the result. The improved scientific and methodical apparatus includes optimal selection criteria and an improved procedure for optimal selection of a certain grouping element. Existing approaches to the selection of optimal strategies for assigning forces and means to perform tasks were analyzed, in particular the criteria of Wald, Hurwitz, and Savage. The peculiarity of this analysis is the examination of the criteria in view of the types of adverse conditions they take into account. The application of these criteria will make it possible to take into account the conditions of uncertainty of the input data and minimize the influence of adverse conditions during distribution. The field of practical use of the analysis results is management processes during preparation for the operation. A procedure of optimal selection of a certain element of the grouping for the performance of a certain task has been improved by using several criteria for choosing the optimal strategy and harmonizing the results of this selection in accordance with the conditions. The proposed procedure guarantees the performance of tasks, and the increase in the value of the objective function can reach 40 %. A feature of the proposed procedure is that the result of choosing the optimal strategy is determined according to the conditions of a certain operation and takes into account various types of adverse conditions. This makes it possible to take into account the factors that significantly affect the uncertainty and minimize the expenditure of resources when performing a certain set of combat tasks. The scope of practical use of the methodology is the process of planning and allocation of forces and means among tasks in the operation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".