Augmented Products: The Contribution of Industry 4.0 to Sustainable Consumption
Bibliographic record
Abstract
This chapter presents the main features and functions of the different technologies related to Industry 4.0. It highlights the opportunities that these technologies present for extending the life cycles of products, whether through improved product design, better access to the product, maintenance, redistribution or reclamation. The chapter also presents a crossover between the literature on Industry 4.0 and on extending the life spans of products (sustainable consumption). The Internet of Things (IoT) transforms autonomous products, such as a table, refrigerator or microwave, into smart products that are connected. The IoT includes remote control and self-monitoring. Artificial intelligence (AI) integrates the production of Big Data via the IoT and various data analysis methods can be applied to such data. The IoT, Big Data and AI are strongly interconnected in reciprocal relationships, while additive manufacturing is at the heart of this process.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".