Sustainable Multidimensional Performance Prediction by ANN-Based Supervised Machine Learning
Bibliographic record
Abstract
Transportation activities have witnessed a significant increase in recent years, with industrial evolution directly impacting the pillars of sustainable development.The literature demonstrates a surge in transport activities, with, for instance, a 20% rise in environmental impact and a 15% increase in overall economic influence.Recognizing this, businesses understand the importance of developing long-term plans for road transportation, considering its substantial impact on sustainability within logistics operations.Consequently, it becomes crucial to construct decision support models capable of analyzing the sustainability of supply chain and logistics performance.Therefore, the objective of this study is to create a prediction model utilizing an Artificial Neural Network (ANN) to approximate the global multidimensional sustainable performance value of the supply chain in the context of road freight transport.This approach combines the key dimensions of sustainability, including economic, social, and environmental aspects, along with operational and stakeholder considerations, for the first time in pursuit of this goal.Prior to the machine learning phase, a minimum condition algorithm was utilized to calculate sustainable performance as an initial design step.This algorithm assigns to a dimension the lowest level among the fields within the same dimension.This study presents a unique technique for predicting the global multidimensional sustainable performance within the logistics industry, which can also be adapted for other sectors.As a result, it offers valuable insights to managers regarding strategic development options.The sustainable performance value provides an indication and quantification of a company's sustainable performance level corresponding to its adherence to and achievement of objectives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".