Development of a performance-based industrial energy efficiency indicator for corn refining plants.
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
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.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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