Research on Supply Chain Toughness Evaluation and Optimization Based on Integrated Learning Algorithm
Bibliographic record
Abstract
Consolidating and improving supply chain resilience and maintaining supply chain stability and security is an important foundation for promoting the realization of high-quality development.After initially selecting supply chain resilience related indicators, the research is screened and downgraded through factor analysis to establish a supply chain resilience evaluation index system.Subsequently, based on the model integration framework, the supply chain toughness evaluation model with improved Stacking integration model is constructed on the basis of a single machine learning algorithm and an integrated learning algorithm, and the model parameters are adjusted and optimized through the learning curve to achieve the optimal evaluation effect and compared with the existing model.The results show that the Stacking supply chain toughness evaluation model constructed in this paper has a relative error of 23% or less in 3685 enterprise samples and accounts for 98.78%.It shows that the Stacking integrated model established in this paper has good prediction effect and high accuracy, which has certain value and significance to the research of supply chain toughness prediction, and can provide scientific reference basis for enterprises.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".