Indoor Environmental Quality Assessment of Historic Buildings in the State Park: A Case Study in Michigan
Bibliographic record
Abstract
Michigan State University and the Michigan Department of Natural Resources established a partnership for sustainable park planning in October 2011. The purpose of the partnership was to enable students in the design fields to work on real-world projects and provide practical solutions. One of the notable projects was to assess old historic buildings in one state park and propose renovation plan to improve its indoor environmental quality and energy efficiency. The buildings in the park functioned as the traveler’s destination in the 1920s and still preserve original interior and exterior features. The team of undergraduates and faculty in interior design visited the park and assessed the interior and exterior conditions of two of its buildings. They used an assessment tool which was designed on a basis of the elements in the indoor environmental quality category (IEQ) of the US Green Building Council’s LEED. Results revealed that the indoor environmental conditions of the buildings should be improved. The energy efficiency of the buildings was low. Based on this assessment, this study offered practical suggestions for improving the building’s indoor environmental quality. This study also proposed an assessment tool for the historic buildings in the state parks in Michigan to assess current indoor environmental quality of those buildings.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".