Inferring drought and heat sensitivity across a Mediterranean forest region in southwest Western Australia: a comparison of approaches
Bibliographic record
Abstract
Changes in climate trends and extreme climatic events are increasingly impacting on forests around the world. In order to better understand how and where major ecological and climatic changes will affect our forested ecosystems, tools based on landscape sensitivity analysis need to be developed to help inform sustainable forest management. This study was undertaken in the Northern Jarrah Forest (NJF) region in the Mediterranean climate of southwest Western Australia. Extreme drought and multiple heatwaves in 2010/2011 resulted in large-scale tree canopy dieback in the NJF. In this study, we used Landsat satellite imagery to (1) accurately map the damaged areas, (2) produce a damage probability model and (3) compare the model with a probability model derived from data collected through an airborne flight survey. We found that the Landsat-derived Normalized Difference Vegetation Index was a good indicator of drought/heat induced damage in the NJF region. Both probability models identified the same set of topography and climate-related factors for determining the probability of drought/heat damage within the landscape. Extrapolation of the Landsat satellite method-based model, however, produced a more deterministic and useful drought/heat damage sensitivity map for the NJF region. The techniques and tools developed, and applied, in this study can readily be transferred to other regions around the world and can assist in the sustainable management and timely climate adaptation efforts to accommodate our future forests.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".