The Process of Management and Control of Feasibility Planning of Road Construction Using the Financial Information System
Bibliographic record
Abstract
Technical and economic planning control and effective organization of management processes in current situations are particularly significant. This is essential to automate orders and handle them effectively. Hence, the solution of tasks associated with planning, accounting, and distribution of orders is of great practical significance. The financial information system that operates at the enterprise does not allow sufficient automation of the enterprise's work and divisions. Analysis of the information system has revealed that automation of document flow and information flows of employees of the "planning and economic Department" is inadequate.Consequently, research objects are the business process - "Technical and economic planning" and the task "Distribution of orders by teams." The subsystem «Technical and economic planning» was produced, indicating the subsystem's tasks and the functions of the tasks. A connection diagram was composed of the tasks of this subsystem with the tasks of other subsystems. The task «Distribution of orders by teams» was expanded and implemented as a software product in the 1C programming environment. The chief issues of ensuring the information security of enterprises and the direction of forming a system of protection of information resources, particularly of limited access resources, are considered. The effectiveness of the implementation of the task “Distribution of orders by teams” were assessed: the annual economic effect of the implementation amounted to 2237, 03 rub.; the cost of developing a software product – 25715,40 rub.; discounted payback period is 11 months.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".