Bibliographic record
Abstract
After incoming the notion of eco-friendly building, many organizations developed green building assessment programs. The total annual energy consumption in the domestic is about 216.5Mtoe, and 11th level of world, greenhouse gas displacement is about 476.1Mton, it's 9th level of the world. About 25% of energy use, about 40% of the consumption of resources, about 50% of CO2 discharge, and about 20~30% of waste discharge are used in the field of Architecture. The purpose of this paper is comparison analysis of domestic and external green building certification system. This paper is analysis of certification system, it is LEED of USA, BREEAM of England, R-2000 of Canada, EnEV of Germany, CASBEE of Japan, NatHERS of Australia, green building certification system of Korea. This is analysis based on assessment items of each system for analysis of certification system. This is through analysis of certification system, grasp concrete problem of domestic certification system and draw a improvement.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".