Heatwave Characteristics in Different Ecosystems across Türkiye: Historical and Future Insights from CMIP6 Simulations
Bibliographic record
Abstract
Heatwaves pose significant threats to diverse sectors, including agriculture and forestry. This extreme weather event is characterized by prolonged periods of exceptionally high air temperatures and has caused substantial economic damage and affecting millions. During heatwave events, agricultural and forest lands are affected by intensified thermal stress and water scarcity, impacting plant health, productivity, and ecosystem stability. This study revealed the projected heatwave changes in frequency and duration over agricultural and forest areas in Türkiye based on the ensemble mean of 23 general circulation models through the two latest CMIP6 climate change scenarios (SSP3-7.0 and SSP5-8.5). Agricultural and forest lands are projected to experience dramatic increases in summer heatwave events and prolonged durations throughout two long-term periods (2041-2070 and 2071-2100) during 21st century, particularly between 36°N and 38°N latitudes. Trend analysis using the triple-ITA method confirms unstable positive trends in historical heatwave metrics over these ecosystems, transitioning to stable positive trends in future projections. These findings emphasize the escalating risk of extreme heat events for critical ecosystems in Türkiye.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".