Representing ecological processes in agent-based models of land use and cover change
Bibliographic record
Abstract
Agent-based models of land use and cover change (ABMs/LUCC) have traditionally represented land-use and land-cover changes as arising from social, economic and demographic conditions, while spatial ecological models have tended to simulate the environmental impacts of spatially aggregated human decisions. Incorporating a dynamic representation of ecosystem processes into ABMs/LUCC can enable new or counter-intuitive insights to be gained into why certain path-dependent outcomes arise and can also spatially constrain model processes, thereby improving the spatial fit of model output against observational data. A framework is therefore provided to assist in determining an optimal approach for representing ecological processes in an ABM/LUCC according to the research question and desired application of the model. Relevant challenges limiting the integration of complex, dynamic representations of ecosystem processes into ABMs/LUCC are then assessed, with solutions provided from recent examples. ABMs/LUCC that use a dynamic representation of ecological processes may be applied to investigate the complex, long-term responses of the coupled human–natural system to a variety of climatic shifts and ecological disturbances.
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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 | 0.001 | 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 | 0.002 |
| 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".