A generic approach to spatial individual-based modelling and simulation of plant communities
Bibliographic record
Abstract
A general framework is proposed for the formulation and simulation of spatially explicit individual-based models of plant communities. A software implementation, siplab , was developed using the R statistical programming language. The scheme is a synthesis that encompasses many approaches from the literature, making possible to compare and combine their different components. Relationships between plant growth and various competition or assimilation indices are discussed, together with the choice of state variables and statistical issues in growth equations. Modelling is extended to deal with environmental heterogeneity, specified as a given resource distribution in the plane. Plants exert competitive pressure over resources at each point, represented by size- and distance-dependent functions that emulate or generalize similar concepts used in existing models. The partitioning of resources where these functions overlap is parametrized in a way that includes the one-sided fully asymmetric allocation of tessellation models, as well as a continuum of symmetric and asymmetric resource sharing alternatives. Finally, the plant resource uptake is integrated over space, with an optional size- and distance-dependent plant response or efficiency weighting. The framework and software permit conducting simulation studies where results are less dependent on any specific model structure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".