Perspectives on offshore wind farms development in Great Lakes
Bibliographic record
Abstract
Our atmosphere is overloading with carbon dioxide and other global warming emissions due to human activities. It causes a web of significant and harmful impacts. Renewable energy resources produce little to no global warming emissions. To date, most of the existing offshore wind farms have been deployed in shallow ocean-coastal areas. The salinity of the ocean averages approximately 35 percent. The Great Lakes with freshwater have shown high potential for installing offshore wind farms, and significant advantages. In this study, the potential capacity for installing offshore wind farms in Great Lakes is discussed based on the wind pattern and speed. Also, it includes the barriers, issues, wind vision, advantages and disadvantages, the most appropriate locations for erecting the offshore wind farms in Great Lakes, updated offshore wind farms, and statistics for decision-makers, interested communities and investors. This paper is among the rare works that have been done in aspect of statistical and data for the wind offshore in Great Lakes as the moratorium in Canadian side and the difficulties in obtaining permissions in the American side put the offshore wind sector on pause for a long time, and recently (since 2016) it started to get some momentum.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".