Climate change impacts and forest adaptation in the Asia–Pacific region: from regional experts’ perspectives
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
Expert opinions have been used in a variety of fields to identify relevant issues and courses of action. This study surveys experts in forestry and climate change from the Asia–Pacific region to gauge their perspectives on the impacts of climate change and on the challenges faced by forest adaptation in the region, and explores recommendations and initiatives for adapting forests to climate change. There was consensus regarding the impacts of climate change on forest ecosystems and on economic sectors such as agriculture and forestry. Respondents also indicated a lack of public awareness and policy and legislation as challenges to addressing climate change. However, the results indicate differences in opinion between regions on the negative impacts of climate change and in satisfaction with actions taken to address climate change, highlighting the need for locally specific policies and research. The study presents specific recommendations to address issues of most concern, based on subregion and professional affiliation throughout the Asia–Pacific region. The results can be used to improve policy and forest management throughout the region. This research will also provide valuable suggestions on how to apply research findings and management recommendations outside of the AP region. The conclusions should be communicated relative to the level of the research and the target audience, ensuring that scientific findings and management recommendations are effectively communicated to ensure successful implementation of forest adaptation strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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