Global GX Business Utilizing Japan's Strength in Precision Die Manufacturing Technology
Bibliographic record
Abstract
With the adoption of the Paris Agreement in 2015, the global push for de-carbonization and the reduction of fossil resource-derived plastics has intensified, supported by various laws and regulations. In Japan, this has led to increased collaboration among industry, academia, and government, particularly concerning Green Transformation (GX) activities. In this context, the paper highlights the efforts of a die manufacturing company, a crucial pillar of the Japanese economy, to advance GX activities from a global perspective. Leveraging its superior die manufacturing technologies, the company is moving downstream in the value chain to acquire new technologies in emerging fields, thereby enhancing its own value. This initiative not only contributes to the development of the regions where the company operates, such as Iida City in Nagano Prefecture and Ohta Ward in Tokyo, but also to the creation of a sustainable society. This report also details the establishment of advanced PLA injection molding technologies (i.e. ultra-thin wall PLA injection molding and heat-resistant PLA injection molding technology). Additionally, it covers the marketing strategies for PLA products and the conceptual framework for creating an organization and system to promote GX activities. Nissin Precision Machines Co., Ltd., renowned for its precision die manufacturing technology since its founding in 1957, serves as a case study to illustrate these efforts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".