Bibliographic record
Abstract
analysis and data in this paper was prepared for a consulting client and we are grateful that they have allowed us to adapt our report in the form of this academic paper. We have modified the plant-specific results in Section 8 to protect the proprietary data and property of our client. 1 1 Executive Summary This paper analyzes a power plant in Alberta, Canada, that is powered by two General Electric LM6000 gas turbines combined with a steam generator that allows combined cycle operations. The LM6000 is derived from a GE engine used on Boeing 767 and 747 airplanes, and is adapted for natural gas by General Electric. This power plant is popular in various power jurisdictions around the world as a turnkey power plant that can offer peaking capacity, and some baseload power delivery. We consider 4 operating modes for the plant: cold metal (off), 15 MW idle in combined cycle, full simple-cycle power (95 MW) and combined cycle full power (120 MW). It is common to refer to such a plant as generating a spark spread: converting natural gas to electricity by burning. A spark spread has two correlated stochastic variables: electricity price and natural gas price. To lower the dimensionality of the problem, we
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 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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".