Review: The use of geographic information systems in wind turbine and wind energy research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper is a review of wind energy articles that use geographic information systems (GIS). It is the hope of the authors that the article will inform renewable energy researchers of the potential for using GIS in their work, and geographers and spatial scientists to learn about the opportunities in wind turbine research. GIS can be used for wind energy planning to determine whether there is adequate wind energy at a site as well as whether the landscape and land-uses are appropriate for wind turbine developments. These types of GIS applications have been used worldwide, typically using previously collected data. To determine which sites are preferable, variables of interest are treated as distinct layers in GIS, and areas that are unsuitable for wind turbine development become evident. Areas that are not preferred for wind turbines are environmentally protected areas or landscapes that cannot be developed effectively. GIS is the ideal tool for identifying preferred sites for wind farms, especially when using decision support systems. Future decision support system research in GIS should consider on-site conditions as well as the opinion of stakeholders and local residents. Involving stakeholders in the decision-making process, either through increased communication or visualization activities that use GIS can lead to higher acceptance of wind turbine installations. Examining the failures and successes of other wind turbine installations may be informative for future developments
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it