Great Lakes Wind Energy Challenges and Opportunities Assessment
Bibliographic record
Abstract
Many issues associated with wind development in the Great Lakes will require solutions different from those developed for offshore wind in ocean states and may not fully benefit from the industry learnings of nearby states. As a result, technology readiness and cost reduction for Great Lakes Wind (GLW) energy generation is likely to be delayed relative to other regions without a substantial, targeted GLW research campaign, and proactive stakeholder engagement in the region at all levels. Failure to conduct the necessary research to lower GLW costs in the near term could limit its contribution to the Nation's decarbonization goals by 2035, and could potentially raise long term energy prices in Great Lakes states if demand for renewable energy continues to accelerate. The overall objective of a research program such as that described in this report would be to enable the realization of commercial GLW before 2035. With the aim of ensuring that prospective development of GLW is conducted efficiently, safely, and coordinated in the best interests of the local residents and stakeholders, the U.S. Department of Energy (DOE) Wind Energy Technologies Office (WETO) tasked the National Renewable Energy Laboratory (NREL) to assist in (a) developing an improved understanding of offshore wind power's development potential in the Great Lakes, (b) identifying the key issues that need to be resolved for this potential to be achieved, and (c) defining a comprehensive research program to address and resolve these issues. This report presents the results of NREL's effort to address these needs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".