Evaluation of Building Materials Based on Sustainable Development Indicators
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
Construction industry regarded as one of the key aspects of achieving the goals of sustainable development in communities. In this regard, the choice of building materials is one of the key challenges in order to improve project performance with respect to sustainable development indicators and the use of sustainable materials, is an effective step towards achieving sustainable construction. This research uses information and evidence, interview and questionnaire prepared (by five points Likert scale method). Also, it has provided expert opinions related indicators widely used in a construction materials, manufacturing process and defining the impact of the production of these materials on sustainable development deals. Validity and reliability of the questionnaires were also performed (with Cronbach's alpha method). As a result of this research, Cement was identified as the most unsustainable material, after that Steel and then Brick and Glass were located with a wide margin. So Light concrete block, Gypsum, Stone, Lime, and Concrete were identified as the most sustainable materials according to existing indicators respectively. The consequences of this study can help the project executors in order to promote the use of sustainable building materials in construction and also industries will be aware of the impact of the sustainability indicators on their products.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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