Evaluation of carbon emission reduction effect of <i>Cryptomeria japonica</i> stand management in Yamagata Prefecture, Japan
Bibliographic record
Abstract
Planted forests are in the utilisation phase in Japan, contributing to the increased use of timber and the realisation of carbon neutrality. We simulated and compared the carbon emission reduction effects of three forest management scenarios, including a no final cutting scenario at two geographical scales, in the context of a privately owned Cryptomeria japonica planted forest in Tsuruoka City, Yamagata Prefecture, Japan. We adopted two substitution approaches: the status quo approach and the dynamic approach in line with Japan’s nationally determined contribution. We used hypothetical stands over three rotation periods of 150 years at the stand level, and a harvest schedule of 120 years at the regional level. The harvesting scenarios based on the status quo substitution approach reduced carbon emissions more than the no final cutting scenario for current wood usage at the study site, although those based on the dynamic substitution approach reduced carbon emissions as much as the no final cutting scenario during the first 30 years. Sustainable timber production from forests may reduce carbon emissions as much as unharvested forests until 2050, indicating that the substitution effects of harvested wood products could have a significant impact on the climate change mitigation effect arising from timber.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".