A COMPARATIVE ANALYSIS OF ACTUAL LOCATIONAL MARGINAL PRICES IN THE PJM MARKET AND ESTIMATED SHORT-RUN MARGINAL COSTS: 2003-2006
Bibliographic record
Abstract
About the Authors Julia Frayer, as Managing Director, co-leads the firm’s market analysis and quantitative business practice area, which involves economic analysis and evaluation of infrastructure assets, detailed modeling of electricity markets, and price forecasting. She has worked extensively in the power sector (covering all aspects of the value chain), as well as other infrastructure industries such as the natural gas sector and the water sector. On regulatory front, she has specialized in areas related to market power mitigation, auction design, and performance-based ratemaking. Dr. Amr Ibrahim is a Senior Consultant with LEI specializing in restructuring, market design, regulation, and operation of wholesale and retail energy markets in the US, Canada, and South America. Dr. Serkan Bahçeci is a Consultant with LEI specializing in empirical analysis and applied econometrics to electricity markets. He has worked extensively on engagements involving generation market power and strategic bidding. Sanela Pecenkovic is a consultant with LEI, providing research and analytical support to market analysis-related engagements in the power sector for markets across North America.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".