Identifying Sustainable Wood Sources for the Construction Industry: A Case Study
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
Wood is generally considered as a sustainable construction material. However, there are not sufficient wood resources in many countries or regions, especially those short of land resources. These countries and regions have to import wood from overseas. Therefore, it is imperative to determine how to choose sustainable importing sources in order to improve the sustainability performance of using wood in construction. This study compares the sustainability performance of wood imported from different regions by considering wood harvesting, manufacture, and transportation. A framework accounting energy consumption and CO2 emissions is developed for sustainability assessment. The results show that importing wood from Canada, Australia, and New Zealand to Taiwan demands a relatively lower amount of energy than from other regions. Specifically, importing wood from Canada (West) demands the lowest amount of energy (2095 MJ/m3), while importing wood form Brazil consumes the highest amount of energy (5356 MJ/m3). In addition, findings showed that the CO2 emissions generated from importing wood from Sweden are significant lower than those from other regions, although the energy consumed during the importing process is relatively high. The study also revealed that the wood manufacturing process and marine transportation contribute to the most energy consumption and CO2 emissions among all importing processes analysed from most of studied regions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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