A neural network-based quantification of risk in large health investments and its applicability to international investment law
Bibliographic record
Abstract
The rapid development of the big health industry brings opportunities for investors, while the risks faced by the investment also increase day by day.This paper combines artificial neural networks and genetic algorithms to construct a risk evaluation model for big health investment based on GA-BP neural network, which provides a tool for analyzing and evaluating the risk of big health investment.The normalization function is used to standardize the model sample data, determine the learning rate, target error and other model parameters, and on the basis of the BP neural network, the genetic algorithm is used to optimize the connection weights and thresholds to achieve the quantitative function of the evaluation of the risk of big health investment.In order to verify the performance of the model to carry out large health investment risk quantification experiments, this paper's model convergence performance is better, the minimum Loss loss value is 0.37, the overall fluctuation is smaller.Compared with BP and PSO evaluation models, the risk evaluation accuracy of this paper's model is above 93.5%, and the comprehensive evaluation accuracy is higher than 91.1%, which can realize more accurate risk prediction, and its applicability in the international investment law has a high practical value.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".