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Record W7096762993

Description

2011· article· en· W7096762993 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationLegislatureGovernment (linguistics)Anticipation (artificial intelligence)ElectricityElectricity market
DOInot available

Abstract

fetched live from OpenAlex

A number of potential pricing issues have been identified by the IMO Market Pricing Working Group in anticipation of the imminent changes to Ontario’s electricity sector as provided for by legislation currently under development by the Government of Ontario. Bill 100 is predominantly enabling legislation. Consequently, a significant number of its provisions will be addressed in greater detail in ensuing regulations. As such, due to the “still in progress ” nature of Bill 100 and the nascent status of its associated regulations, there is presently insufficient information on which to identify and/or anticipate all related pricing issues with any great detail or depth. For the time being, the IMO Market Pricing Working Group has identified two general issues that arise from the Working Group’s understanding of Bill 100 as currently drafted (i.e. as introduced to the Legislative Assembly of Ontario for first reading). These issues encompass: (a) Concerns relating to the offering behaviour of “Heritage Pool ” generators – particularly where such generators would be self-scheduling- and associated impacts (whether complementary or distortionary) on market efficiency and reliability; and (b) Concerns relating to ensuring meaningful linkages between wholesale and retail prices, broadly in view of promoting effective consumer participation in electricity and contract markets and facilitating demand response. Until such time as these and other related issues become better defined, this issue summary shall remain “as is ” pending further information, discussion and prioritization.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.155
Teacher spread0.131 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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