Modelling Global Deforestation Using Spherical Geographic Automata Approach
Bibliographic record
Abstract
Deforestation as a land-cover change process is linked to several environmental problems including desertification, biodiversity loss, and ultimately climate change. Understanding the land-cover change process and its relation to human–environment interactions is important for supporting spatial decisions and policy making at the global level. However, current geosimulation model applications mainly focus on characterizing urbanization and agriculture expansion. Existing modelling approaches are also unsuitable for simulating land-cover change processes covering large spatial extents. Thus, the objective of this research is to develop and implement a spherical geographic automata model to simulate deforestation at the global level under different scenarios designed to represent diverse future conditions. Simulation results from the deforestation model indicate the global forest size would decrease by 10.5% under the “business-as-usual” scenario through 2100. The global forest extent would also decline by 15.3% under the accelerated deforestation scenario and 3.7% under the sustainable deforestation scenario by the end of the 21st century. The obtained simulation outputs also revealed the rate of deforestation in protected areas to be considerably lower than the overall forest-cover change rate under all scenarios. The proposed model can be utilized by stakeholders to examine forest conservation programs and support sustainable policy making and implementation.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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".