Development of a performance-based industrial energy efficiency indicator for corn refining plants.
Bibliographic record
Abstract
Organizations that implement strategic energy management programs have the potential to achieve sustained energy savings if the programs are carried out properly. A key opportunity for achieving energy savings that plant managers can take is to determine an appropriate level of energy performance by comparing their plant's performance with that of similar plants in the same industry. Manufacturing facilities can set energy efficiency targets by using performance-based indicators. The U.S. Environmental Protection Agency (EPA), through its ENERGY STAR{reg_sign} program, has been developing plant energy performance indicators (EPIs) to encourage a variety of U.S. industries to use energy more efficiently. This report describes work with the corn refining industry to provide a plant-level indicator of energy efficiency for facilities that produce a variety of products--including corn starch, corn oil, animal feed, corn sweeteners, and ethanol--for the paper, food, beverage, and other industries in the United States. Consideration is given to the role that performance-based indicators play in motivating change; the steps needed to develop indicators, including interacting with an industry to secure adequate data for an indicator; and the actual application and use of an indicator when complete. How indicators are employed in the EPA's efforts to encourage industries to voluntarily improve their use of energy is discussed as well. The report describes the data and statistical methods used to construct the EPI for corn refining plants. Individual equations are presented, as are the instructions for using them in an associated Excel spreadsheet.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".