Bibliographic record
Abstract
The article explores the transformative impact of Artificial Intelligence (AI) on engineering, focusing on the evolution over the last decade. AI has become a cornerstone in reliable engineering, influencing various aspects such as predictive maintenance, fault detection, optimization, automation, and decision support. Predictive maintenance, enabled by AI algorithms, revolutionizes traditional approaches by analysing extensive datasets to predict equipment failures, allowing proactive interventions and minimizing downtime. Fault detection and diagnostics benefit from AI's real-time monitoring and early anomaly identification, reducing the risk of catastrophic failures and enhancing overall system reliability. Optimization of complex systems is facilitated by AI's capacity to process vast amounts of data, leading to improved performance and minimized resource consumption. The integration of AI in automation and robotics reshapes manufacturing processes, emphasizing precision and reliability. Simulation and modelling, data analysis, and supply chain optimization are also discussed as vital areas where AI contributes to enhanced reliability. The article highlights the importance of ethical considerations and human oversight in deploying AI responsibly, emphasizing a collaborative synergy between AI and human expertise for continued advancements in engineering solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".