Proposal of industrial product service system for oil sands mining
Bibliographic record
Abstract
As new materials, new technology such as cloud computing and new configurations for the manufacturing enterprise emerge, the distinctions among mechatronics, manufacturing, and service industries become blurred. Consequently, it is expected that the definitions of Industrial Processes become even broader. Outsourcing industrial processes is a common practice and also strategy weapon for many companies to reduce costs and enhance the utilization of internal resources forming core competencies. This paper presents the concept of an industrial product-service system for oil sands mining (om-iPSS) based on the utility enhancement of oil sands mining systems. The intelligent technologies such as the cloud computing are proposed as embedding knowledge in the outsourcing-oriented om-iPSS for the whole life cycle of production process. As a result, the om-iPSS with advanced characteristics such as distributed, intelligence, adaptation and self-organization is developed in which a novel approach for enabling individualized and highly-customized production is proposed. Architecture is proposed to enable the om-iPSS from the aspects of both hardware and software. Key techniques for implementing the architecture are discussed for illustrating the use of the om-iPSS concept.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".