Preliminary sensitivity study on an life cycle assessment (LCA) tool via assessing a hybrid timber building
Bibliographic record
Abstract
In order to address concerns related to global warming and increased atmospheric carbon content, the life cycle assessment (LCA) tool has demonstrated usefulness in the building and construction sector. The LCA is used to evaluate environmental impacts concerning all stages of the building process from “cradle” to “grave”. The LCA helps promote sustainable development by considering environmental indicators such as stratospheric ozone depletion , eutrophication , global warming potential , and many more. It is of an interest to know the degree of impact on a given environmental indicator if an input is changed in terms of the type or amount of the materials used. The LCA software Athena IE4B was employed to analyze data of a selected timber building. This study was aimed at evaluating the sensitivity of LCA analysis on a hybrid timber building, which was done via two case studies . Case 1 focused on changes in the volume of wood materials, meanwhile Case 2 focused on simultaneous changes in the volume of materials for wood, steel, and concrete. In Case 1, it was observed increasing wood materials increased environmental indicators, with stratospheric ozone depletion being the most sensitive and global warming potential as the least sensitive. Case 2 discovered that proportionally increasing wood materials in relation to steel and concrete materials decreased environmental indicators, with eutrophication being the most sensitive and stratospheric ozone depletion as the least sensitive. This study helped support the feasibility of using Athena IE4B for LCA analysis in the initial assessment of a building.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".