Replacement of Lighting Fixtures with LED Energy Efficient Lights at the Parking Facility, Milwaukee, Wisconsin
Bibliographic record
Abstract
The Forest County Potawatomi Community (FCPC or Tribe) owns a six-story parking facility adjacent to its Potawatomi Bingo Casino (the Casino) in Milwaukee, Wisconsin, as well as a valet parking facility under the Casino (collectively, the Parking Facility). The Parking Facility contained 205-watt metal halide-type lights that, for security reasons, operated 24 hours per day, 7 days per week. Starting on August 30, 2010, the Tribe replaced these fixtures with 1,760 state-of-the-art, energy efficient 55-Watt LED lights. This project resulted in an immediate average reduction in monthly peak demand of 238 kW over the fourth quarter of 2010. The average reduction in monthly peak demand from October 1 through December 31, 2010 translates into a forecast annual electrical energy reduction of approximately 1,995,000 kWh or 47.3% of the pre-project demand. This project was technically effective, economically feasible, and beneficial to the public not only in terms of long term energy efficiency and associated emissions reductions, but also in the short-term jobs provided for the S.E. Wisconsin region. The project was implemented, from approval by U.S. Department of Energy (DOE) to completion, in less than 6 months. The project utilized off-the-shelf proven technologies that were fabricated locally and installed by local trade contractors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".