International Trade Relations of Products for Wind Energy Production: A Study from the Dynamic Social Network Analysis (DSNA)
Bibliographic record
Abstract
The objectives of this work are to check the international trade relations of the top 20 countries that sell products related to the production of wind energy and which are the main products in international trade in this sector. The motivation for the analysis of this energy matrix stems from the need of more consistent public policies for production, because the logistics necessary to reach the end consumer, as this energy source needs to be installed in places with good intensity winds, and later transported to the consumer market. Initially they selected 23 products that make up the equipment for building towers and the production and processing of wind energy. Using the data in COMTRADE, it was the countries that sell the products chosen in order to establish the main countries. Later, he collected the data from 2006 to 2015, from the HS codes. Secondly the export data of the 23 products were grouped to check the major global players in the sale and purchase of these products then added to the annual sales of each country for each product in order to see which are prevalent in each country. Data were analyzed using the Social Network Analysis (SNA) with the aid of Gephi software. For graphical presentation of the data is used dynamic networks that allow the visualization of the change in exports over the period studied. It is observed that trade relations between the US, Canada and Mexico are the most expressive of the network, but you must also highlight Japan, Germany and France and in recent years the strengthening of Denmark in this market. Four products stand out, which say about the wind turbine blades and towers; consoles for a voltage not exceeding 1000V, instrument control for automatic adjustment and gear boxes and other speed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".