Bibliographic record
Abstract
Based on 300 listed companies report data classification and analysis, founding canonical discriminated function, and through empirical analysis, the test results indicate canonical discriminated function of the Chinese banking market discriminated validity of the credit of enterprises, for the investment banks to provide constructive recommendations and sensible way to evade credit risk, is also conducive to an accurate evaluation of the credit situation. Key words: Commercial banks, Credit risk investment decision, Function Resume: L’article present procede a l’etude des risques de decision dans l’investissement du credit. L’auteur classe, analyse, avec la theorie de la statistique, les donnees de l’annuaire de 300 societes cotees et en construit la function de discrimination typique. Le resultat montre que la fonction de discrimination typique joue un certain role pour les banques commerciales dans l’appreciation du credit des entreprises. Elle peut fournir des conseils constructifs pour la decision d’investissement des banques, aide a eviter raisonnablement les risques de decision dans l’investissement du credit et a juger correctement le credit d’une entreprise. Mots-cles: banques commerciales, risques de decision dans l’investissement du credit, fonction 摘 要： 本文通過對現行信貸投資決策風險進行分析，運用統計學理論對300 家上市公司的年報資料進行歸 類、分析，建立典型判別函數，經過實證分析，檢驗結果顯示典型判別函數在商業銀行判別企業信用時具有 一定的效用，能夠為銀行的投資決策提供建設性的建議，理智地回避信貸投資決策風險，同時也有利於銀行 準確地評價一個企業的信用情況。 關鍵詞： 商業銀行；信貸投資決策風險；函數
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".