An attempt to create the holistic flow chart of forest resources
Bibliographic record
Abstract
Abstract The environmental development of a building has an important role to play in terms of the sustainable development of our society. Against this background, wood and wood-based products have lately attracted notable attention as promising construction materials due to then-unique environmental properties, such as renewability and carbon storage capacity. For the sustainable use of wood, a relevant balance between supply and demand of forest resources is significant. However, little work has been conducted on a comprehensive understanding of forest resources flow. The objective of this study was. therefore, to draw an integrated chart of forest resources flow including forest conditions, round wood production, wood product manufacturing, wood stock in buildings and waste management at the end of product life, mainly focusing on building material flow. The flow charts were drawn for five countries (Japan. Finland. Germany, Sweden and Canada) based on statistical data. Instead of directly showing numerical information, the chart is presented as an infographic in order to promote intuitive understanding of the contents. The chart is intended as a basis for the development of sustainable forest resource circulation. This paper focuses on introducing the chart as such ad briefly discusses how the chart can be utilized for further study in relation with SDGs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".