Semantic-Driven Internet of Behaviours for Enhancing Supply Chain ESG Capabilities Through Generative AI
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
Pursuing sustainable development goals requires enterprises to enhance their environmental, social, and governance (ESG) capabilities. In logistics and supply chain management, where small and medium enterprises dominate, integrating ESG practices is challenging and often favors larger companies with established frameworks. This study introduces an ESG recommendation system based on generative artificial intelligence (GERS) to provide accessible, tailored ESG guidance. Leveraging large language models and an ESG knowledge base, GERS offers actionable recommendations, particularly benefiting small and medium enterprises. Evaluated through a case study with a Hong Kong Logistics Association ESG assessment programme, expert panels confirmed the quality of its recommendations. Results demonstrate the GERS's ability to generate ESG improvement plans, enhancing capabilities efficiently. This research highlights the transformative potential of generative artificial intelligence in fostering sustainability, showcasing its role in creating adaptive, context-aware services that drive collaborative learning and sustainable practices in supply chains.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| 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