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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".