A comprehensive decision-making approach: evaluating and managing risks associated with emerging technologies – a LineDrone technology case study
Bibliographic record
Abstract
The primary objective of this paper is to conduct an exploratory, qualitative study in Industrial Asset Management, focusing on the development of an Integrated Decision-Making Framework. This framework is designed to evaluate performance variability caused by emerging technology risks and extreme, rare, and disruptive events. To achieve this, we integrate two robust decision-support tools: the Functional Resonance Analysis Method (FRAM) and the System-Theoretic Process Analysis (STPA). FRAM provides a comprehensive analysis of sociotechnical system functions, highlighting their interconnections and dependencies. In contrast, STPA, based on the System-Theoretic Accident Model and Processes (STAMP), takes a top-down approach to hazard assessment. The integration of FRAM and STPA aims to create a powerful decision-support framework. A case study on the LineDrone, which inspects high-voltage transmission lines without direct human interaction, demonstrates the framework's effectiveness in managing performance variability within complex sociotechnical environments.
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 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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".