A mixed-integer non-linear programming model for CO<SUB align=right>2 emission reduction in the power generation sector
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
Electricity generation is considered to be one of the main contributing sources to the air pollution problem. It is, therefore, important to develop and implement effective control strategies to prevent the expected abrupt increase in emissions from this sector. Any control strategy must be suitable for local implementation and must also be economically viable. The main objective of this paper is to present optimisation models that can be used to determine the most cost effective strategy or combination of strategies to reduce CO2 emissions to a specific level. Optimisation results for an existing network of power plants show that it may be possible to reduce CO2 emissions by increasing power plant efficiency through a variety of adjustments in the plants. These include fuel balancing, fuel switching, and the implementation of improvement technologies to existing power plants to increase their thermal efficiency.
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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 | 0.000 |
| 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