A graduate level course on economic metrics and innovative finance mechanisms for wind
Bibliographic record
Abstract
Abstract A graduate level course on economic metrics and innovative finance mechanisms for has been developed. Topics include non-traditional financing structures for project development and the application of economic metrics to investment decision making. The development of this course was motivated by recent surveys and through consultation with wind energy professionals. Recent surveys have indicated that highly qualified persons entering renewable energy fields are lacking knowledge of the technical, economic, and policy-connected aspects of their work. Synthesis of these discussions, which have guided the development of course content, highlighted that an understanding of market policy and project economics would greatly enhance the efficacy of energy technical professionals. The purpose of this course is to develop holistically equipped wind energy professionals who can intelligently engage with business and finance professionals in their field. Both live instruction and online versions of this course intend to provide this knowledge through application-based lectures and case studies to set graduates of this course apart from their peers. Course delivery, topics, learning outcomes, and evaluation methods are provided.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.073 | 0.027 |
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".