Energy star product specification development framework: Using data and analysis to make program decisions
Bibliographic record
Abstract
The Product Development Team (PD) in the US Environmental Protection Agency's ENERGY STAR Labeling Program fuels the long-term market transformation process by delivering new specifications. PD's goal is to expand the reach and visibility of ENERGY STAR as well as the market for new energy-efficient products. Since 2000, PD has launched nine new ENERGY STAR specifications and continues to evaluate new program opportunities. To evaluate the ENERGY STAR carbon savings potential for a diverse group of products, PD prepared a framework for developing new and updating existing specifications that rationalizes new product opportunities and draws upon the expertise and resources of other stakeholders, including manufacturers, utilities, environmental groups and other government agencies. By systematically reviewing the potential of proposed product areas, PD makes informed decisions as to whether or not to proceed with developing a specification. In support of this strategy, PD ensures that new product specifications are consistent with the ENERGY STAR guidelines and that these guidelines are effectively communicated to stakeholders during the product development process. To date, the framework has been successful in providing consistent guidance on collecting the necessary information on which to base sound program decisions. Through the application of this framework, PD increasingly recognizes that each industry has unique market and product characteristics that can require reconciliation with the ENERGY STAR guidelines. The new framework allows PD to identify where reconciliation is needed to justify program decisions.
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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 | 0.130 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".