A Study on the Method of Analyzing Accounting Statements Based on Combinatorial Mathematics
Bibliographic record
Abstract
This paper proposes an accounting statement evaluation model based on hierarchical analysis algorithm (AHP)-fuzzy comprehensive evaluation (FCE) under the theory of combinatorial mathematics. The initial evaluation index system is determined based on the principles of evaluation index system construction, and after the Delphi method screening, the final accounting statement evaluation index system is composed of 14 secondary indicators and 5 primary indicators. Using hierarchical analysis algorithm to calculate the weights of the indicators, and substituting the calculated weights into the comprehensive fuzzy matrix to finalize the task of evaluation and analysis of accounting statements. The first-level indicators are Solvency A2 (0.1680) < Profitability A1 (0.1797) < Operating Capacity A4 (0.1971) < Cash Capacity A5 (0.2093) < Development Capacity A3 (0.2459), while the weights of the second-level indicators are distributed in the range of [0.0174, 0.2079]. The comprehensive evaluation score of the accounting statement of X Breweries Group Company is 73.31, indicating that the overall condition of the company's accounting statements is good.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".