A Qualitative Study of Green Building Indexes Rating of Lightweight Foam Concrete
Bibliographic record
Abstract
Green building index is considered as the rating tool for evaluating the siting, design and performance of buildings and infrastructures based on worldwide acceptable six main criteria’s It was invented after the Kyoto protocol, Japan, on 11th of December 1997 and the adoption in Marrakesh in 2001 by the United Nations and her subcommittee. It was developed in the built environment industry by the Government support of each country to combat the issue of green house gas emission. Carbon dioxide is acclaimed as one of the main greenhouse gas emission which is mainly through the activities of human race in the world resulting into global warming hence the effort to make the environment lighter enough to inhabit. Construction industry was assessed through studies to be contributing 5% of the world total carbon dioxide emitted through cement production. It was also claimed that a tonne of concrete produces carbon dioxide in the range of 0.05 to 0.13 tonnes. Foam concrete being a new innovative green technology material for sustainable building and civil construction needs to fulfill the criteria’s of this rating tools for it to be considered as sustainable materials. This paper study the assessment of this lightweight concrete material in view of green building index criteria’s and the result are hereby analyze and concluded that foam concrete can be effectively used as sustainable material for building and civil engineering construction.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".