Assessment and Adaptation of an Appropriate Green Building Rating System for Nigeria.
Bibliographic record
Abstract
The practices used in rating green buildings are constantly evolving and differ from place to place; there are fundamental principles that persist from which the rating is derived e.g. siting and structure, design, energy, water and material efficiency, indoor environmental quality, enhancement, operations and maintenance optimization, and waste and toxic reduction. The essence of green building is an optimization of one or more of these principles. This paper presents a comparative analysis of seven well-known sustainable rating systems – BREEAM, CASBEE, GREEN GLOBES, GREEN STAR, HK-BEAM, IGBC Green Homes and LEED by the perceptions and opinions of stakeholders in Nigeria certified in green building rating systems in an attempt to select and adapt a green building rating system for Nigeria. Various aspects of these systems were scrutinized and analyzed in order to find out the best option for the Nigerian built environment. Based on the findings of this study the green building rating systems LEED which is the dominant system in the United States and Canada is appropriate for Nigeria because it helps costumers determine environmental performance, with strong base, large investments and proven advantages scored the highest with 80 points out of 100 points. Keywords: architecture , built environment, green building rating system, Nigeria green building council, sustainability,
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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 | 0.007 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".