Bibliographic record
Abstract
The development of science and technology has made people's quality of life better and better, and the mature use of electricity has made people's lives more convenient and faster. However, this has also led to a drastic increase in people's use of non-renewable energy such as coal and oil. In 2021, about 4,108 billion kilowatt-hours (kWh) (or about 4.11 trillion kWh) of electricity were generated at utility-scale electricity generation facilities in the United States, about 546 million short tons (MMst) of coal were consumed. (EIA). In such situation, the exploitation and use of renewable energy becomes significantly urgent and necessary. From 2008 to 2018, global installed wind power capacity grew by an average of 17.2% per year (BP Statistical Review of World Energy 2020). On IRENA's (2019) transformation roadmap, to stay on the pathway of 1.5 °C warming, wind energy is projected to generate 35% of the total electricity demand by 2050. My study area for my research is in Yukon, it is in south part of Canada and contains 4.7 percent of total area. The aim of my project is to determine the suitability of Wind Farm Expansion and its Electric Applications Radiation for Yukon area. The method for the project is using reclassify analysis tool in order to find the suitability site for constructing the wind farms. 1. How Much of Each Energy Source Does It Take to Power Your Home. (2017, September 29). McGinley Support Services. https://www.mcginley.co.uk/news/how-much-of-eachenergy-source-does-it-taketo-power-your-home/bp254/2. CER – Provincial and Territorial Energy Profiles – Yukon. (n.d.).https://www.cerrec.gc.ca/en/data-analysis/energy-markets/provincial-territorialenergyprofiles/provincial-territorial-energy-profiles-yukon.html3. Just a moment. . . (n.d.). https://www.researchgate.net/figure/Flow-Diagram-of-aWindTurbine-System-Here-1-Wind-Turbine-Converts-wind-energy- into_fig3_307906589
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".