Bibliographic record
Abstract
Accurate enterprise credit evaluating can efficiently avoid the asymmetry of the information which the finance institute transferred ,on the other hand ,the enterprise can bring the financing cost down. According to the loan examine and approve work of our country and stock industry bank, the index system of ECE is constructed. Then, a new Enterprise Credit Evaluating Model Based on Gray theory and fuzzy mathematics is proposed. Finally, a example is given and the result show good reliability. Keywords: ECE (Enterprise Credit Evaluate), Gray System Theory, White function, AHM（Attribute Hierarchical Model）, FCE(Fuzzy Comprehensive Evaluate) Resume L’evaluation precise du credit d’entreprises peut d’un cote eviter de facon efficace l’asymetrie de la transmission des informations des institutions financieres, de l’autre, elle peut permettre aux entreprises de baisser les couts financiers. Cette these relie ensemble la verification et l’approbation du credit de la banque du commerce nationalisee et de la banque industrielle par societe anonyme pour creer un systeme d’evaluation du credit d’entreprises( ECE). Selon les theories d’evaluation grises basees sur AHM et des methodes des mathematiques ambigues, elle a fait une proposition d’un nouveau modele d’evaluation qualitative et quantitative. Enfin, un exemplaire est donne et le resultat nous montre une bonne credibilite et precision. Mots-cles: ECE (evaluation du credit d’entreprises), theorie d’evaluation grise, fonction blanche, AHM（modele d’attribuation hierarchique）, FCE(methodes des mathematiques ambigues) 摘 要 準確的企業資信評級一方面可以有效的避免對金融機構資訊傳遞的非對稱性，另一方面企業也可以降低融資成本。文章結合我國國有商業銀行和股份制商業銀行信貸審批工作，構建企業資信評估指標體系，通過基於 AHM的灰色評價理論和模糊數學的方法，提出了一種新的定性和定量相結合的評價模型，實證結果表明，該模型得到的企業資信評級結果有較高的可信度和準確度。 關鍵詞：資信評級；屬性層次；白化權函數；灰色評價；模糊綜合評判
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".