Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Financial distress prediction, the crucial link of enterprise risk management, is also the core of enterprise financial distress theory. With currently global economic recession and the gradual perfection of artificial intelligence technology, the study in this paper begins by optimizing the back-propagation (BP) neural network model using the genetic algorithm (GA). In doing so, it can overcome the deficiency that the BP neural network model is slow in convergence and easily trapped into local optimal solution. The study then conducts training and tests on the optimized GA-BP neural network model, using financial distress data from Chinese listed enterprises. As can be seen from the experimental results, the optimized GA-BP neural network model is significantly improved in terms of the accuracy and stability in financial distress prediction. The study in this paper not only provides an effective test model for the automatic recognition and early warning of enterprise financial distress, but also contributes to new thoughts and approaches for the application of artificial intelligence in the field of financial accounting.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it