Parameterizing open-source energy models: Statistical learning to estimate unknown power plant attributes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Energy systems models are used to perform energy and environmental policy analysis, inform company strategy, and understand implications of technological change. Although open-source models can promote transparency and reproducibility, data availability and cost can be prohibitive barriers for researchers and other stakeholders. This paper presents a novel application of a statistical approach to predict unknown power plant parameters in Canada using available data from the United States, which can be applied in other settings where critical model inputs are missing. We apply two statistical learning methods, linear regression and k-nearest-neighbors, and compare their performance on unseen portions of the United States data before applying the learned functions to unknown Canadian data. Results indicate that reasonable predictions of heatrates and, to a lesser extent, operation and maintenance costs are possible even with limited data about age, capacity, and power plant types. The nearest-neighbor approach generally outperforms linear regressions for the datasets and applications to power plant parameters investigated here.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it