Temperature Variation and Climate Resilience Action within a Changing Landscape
Bibliographic record
Abstract
Temperature change can have profound impacts on livelihood activities and human well-being. Specific factors such as land transitions and climate knowledge can influence temperature variation and actions for adaptation. In addition to meteorological data, this study integrates land surface temperature (LST) derived from satellite imagery and local temperature perceptions obtained through interviews to advance a deeper understanding of spatial temperature and its impacts, which is not often seen within climate studies. This study examines local temperature across three different land types (rural mountains, rural agricultural lowlands, urban areas) in the Greater Angkor Region of Cambodia to highlight important insights about temperature and climate resilience action. The results revealed that changes in temperature were most pronounced in Phnom Kulen National Park (rural mountain) and in the rural agricultural lowlands, where residents discussed direct impacts and disruptions to their lives. Temperature, in both the LST results and through local perceptions, demonstrated a strong correlation to ground features, where areas with low vegetation exhibited high temperatures and areas with high vegetation observed low temperatures. While climate action in the form of tree planting and forest conservation are major climate mitigation strategies being undertaken in this region, social awareness and the ability to adapt to changes in temperature was revealed to be uneven across the landscape, suggesting that local entities should mobilize around gaining more education and training for all residents.
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.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".