Adapting for Uncertainty: A Scenario Analysis of U.S. Technology Energy Futures
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
Policymakers and managers in the U.S. energy sector will face complex multidimensional challenges as they confront potential supply shortfalls, infrastructure constraints, and environmental limitations in the years ahead. Using a technique known as scenario analysis, this paper investigates key energy issues and decisions that could improve or reduce the ability of the United States to deal with the uncertainties that may challenge the U.S. economy during the next fifty years. Four scenarios have been developed representing a diverse range of future worlds to explore the driving forces and critical uncertainties that may shape U.S. energy markets and the economy for the next fifty years. Each scenario has been quantified using a computable general equilibrium model, the All Modular Industry Growth Assessment model, also known as the AMIGA modeling system. The preliminary results from the scenario analysis suggest that the range of feasible U.S. energy futures is broad, but that energy use is expected to grow under all scenarios.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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