<b>Analysing</b> Causal dependencies of composite service resilience in cloud manufacturing using resource-based theory and DEMATEL method
Bibliographic record
Abstract
The purpose of this paper is to construct a causal model of dimensions and their attributes that influence composite service resilience in Cloud manufacturing (CM) system. The composite services are regarded as critical components of CM to accomplish manufacturing jobs and are executed in a distributed, heterogeneous and autonomous environment with high uncertainty and dynamicity. The dimensions and attributes of the proposed model were first identified based on resource-based theory and related literature. Then, the DEMATEL technique was used to measure the strength of influence among the studied factors. The required data were collected through the questionnaires replied by experts from industry and academia. The results of data analysis indicate that virtual resource pool and elastic resource management have the most impact on composite service quality of resilience. This study presents a novel causal model to improve the existing knowledge on composite service resilience in the context of CM. Furthermore, the research findings provide system analysts and designers with a clear definition of composite service resilience. They are useful to design explicit strategies for improving the resilience level of the composite services at different layers of CM architecture in practice.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".