Offer Behaviour Enforcement Guidelines For Alberta’s Wholesale Electricity Market
Bibliographic record
Abstract
specific prohibitions some of which have application to market participant offer behaviour in the Alberta electricity market. In the absence of jurisprudence, the Market Surveillance Administrator (MSA) believes it is helpful to stakeholders to explain our analytical framework and how we intend to enforce the provisions in the Regulation with potential application to offer behaviour. We have done so in this document, called the Offer Behaviour Enforcement Guidelines. The development of the guidelines was the subject of stakeholder engagement, commencing with a roundtable in February 2010 and culminating in this release in January 2011. The engagement process sought input from interested stakeholders with a view to testing and informing the MSA’s views prior to the finalization of the document. The approach taken has been to build upon the experience gained since the opening of the Alberta market, draw on relevant experiences from other electricity markets and set the document within the context of well established analytics from the domain of competition law and economics. The guidelines strive to provide transparency and predictability regarding the MSA’s assessment of market participant offer behaviour so that participants can govern themselves accordingly. The document goes beyond enforcement in the narrow sense of the term to explain our approach where
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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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".