ERL-118550 Data: Rebates and Grid Decarbonization from the Inflation Reduction Act Promote Equitable Adoption of Energy Efficiency Retrofits
Bibliographic record
Abstract
The authors have self-reported an issue in how they used RSMeans 2019 City Cost Index (CCI) data to adjust for regional cost differences. The publicly available webpage stated these data could be used to “adjust for cost differences when compared to the national average, show cost differences between cities, compare cost differences between quarters of the same year, or adjust costs to Canadian cities.” However, the RSMeans Data and Engineering Department later clarified that these values “were intended to show how much CCI values changed for each city at the start of 2019 compared to the values in our 2019 book.” Nevertheless, our capital cost estimates closely align with several peer-reviewed studies and publicly available data sources. Based on our review, we do not believe our method significantly affected the study’s overall findings or conclusions. Further discussion is provided in the manuscript’s Limitations section and Appendix S5 of the Supplementary Materials. Peer-reviewed article available here: https://iopscience.iop.org/article/10.1088/1748-9326/adb765 Article DOI: 10.1088/1748-9326/adb765
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.029 |
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".