Bibliographic record
Abstract
ABSTRACT We model medium- and long-term Alberta power prices by identifying the primary price drivers and characterizing their dynamics in an engineering-based bottom-up model. This fundamental model is based on the economic theory of supply and demand. Power prices will be represented naturally while satisfying operational constraints. In view of the uncertainty around their future values, we model independent exogenous variables such as fuel prices, outages and load as stochastic processes. The model simulates bid stack by incorporating historical bidding behavior. This simulated bid stack model is an original, creative approach to modeling not just spot prices but also different risk measures and forward contracts. The interactions of simulations of different factors produce a distribution of prices, with a probability associated with each price range, rather than having a single price. While common optimization models in the literature mimic the role of a market administrator, our simulation-based model aims to combine the influences of a wide range of underlying variables in a mathematical approach. Fundamental models can potentially be used for delta analysis, scenario/sensitivity analysis, measuring risk matrixes, market-price-of-risk analyses, development of trading strategies and decision support for investments or acquisitions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".