Analysis and Selection of Marine Engineering Equipment Manufacturing Industry Developing Strategy Based on Diamond Model - Take Guangdong Province as an Example
Bibliographic record
Abstract
With the over-exploitation of global resources and the increasing cost of developing land resources, marine resources have become the new choice for coastal countries to address resource problems. The development and utilization of marine resources encourages the growing of marine engineering equipment manufacturing industry. In this paper, we take Guangdong Province as the studied area, which has a natural advantage for the development of shipping and marine equipment industry, applying the mainstream industry competitive advantage theory – “diamond model” to build evaluation index system. And we calculate combining weights by AHP and variation coefficient method, as well as giving a comprehensive evaluation from the perspective of quantitative analysis for development of marine engineering equipment manufacturing industry in Guangdong Province. The results show that although Guangdong marine engineering equipment manufacturing industry achieves rapid development in recent years, the total scale amount of industry is small, shipbuilding industry development is slow, and professional technical personnel is inadequate. For these problems, this paper provides some suggestions for marine equipment manufacturing industry in Guangdong Province.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".