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Record W3003261066 · doi:10.3808/jeil.201900022

A GIS-based Decision-Making Support System for Wind Power Plant Site Selection, Case Study for Saskatchewan

2019· article· en· W3003261066 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Environmental Informatics Letters · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsSaskatchewan Ministry of AgricultureGovernment of SaskatchewanMcMaster UniversityUniversity of Regina
Fundersnot available
KeywordsSite selectionWind powerGeographic information systemPower stationEnvironmental resource managementSelection (genetic algorithm)Environmental scienceEnvironmental economicsComputer scienceEngineeringGeographyCartography

Abstract

fetched live from OpenAlex

The increasing developments of wind power plants occur in many countries, which are used to mitigate the adverse effects of fossil fuels on the environment. In consideration of negative impacts, wind energy should be systematically analyzed in order to optimize the plans of governments and developers. A decision-making support system for wind power plant site selection was developed by using geographical information system in this study. The environmental, economic, and technical factors are invoked to generate the methodology. By comparing the overall performance index from the results, the best locations for wind power plants can be selected. The methodology was applied to the case study of Saskatchewan, where the development of wind power plant could be considered urgent. The results demonstrate that Saskatchewan has great potential for wind power energy development and southwest Saskatchewan is the most favorable area.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.260
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it