Converging on human-centred industry, resilient processes, and sustainable outcomes in asset management frameworks
Bibliographic record
Abstract
Abstract The objective of increasing productivity while optimizing operational and organizational processes has focused Industry 4.0 (I4.0) on technological development without considering the impact of technology on people and the impact of mass production on the environment. These impacts have led to growing concerns about climate change and complex global risks. A new vision of the industry, called Industry 5.0 (I5.0), has emerged within the scientific community. This human-centred industry appears to be a bold turn from individual technologies to a systematic approach that enables industry to achieve societal and environmental goals beyond economic growth. Under this approach, the question is no longer whether asset management should change, but what that transformation should look like. This paper identifies areas for improvement of the asset management process and presents a framework that incorporates the core values of I5.0 within the overall asset management framework, in which the core principles remain, and the new technologies are the enabling functions. Though the primary focus of this paper on manufacturing and industrial systems, many of its concept and ideas are also relevant to asset management in the public sector infrastructure systems.
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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 | 0.018 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.054 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".