Linear Regression Results for Simulated Forest Epidemic Entropy Model

Linear regression results for the mean scaled entropy model from a simulation study. The model explains 68.2% of the variance in mean scaled entropy across 90,000 simulated forests. The dataset was authored by Cyril Geismar and last updated on June 1, 2026.

Use Cases

• Validate the linear relationship between epidemic size and mean scaled entropy as described in the model.
• Assess the influence of the reproduction number (R0) on epidemic entropy based on the model coefficients.
• Analyze the effect of the dispersion parameter (k) on the predictability of simulated forest epidemics.
• Reproduce the statistical analysis of simulated epidemic entropy described in the associated research.

Strengths

• Model performance is quantified with a reported R-squared value of 0.682.
• Analysis is based on a large simulation of 90,000 forests.
• Dataset is openly licensed under CC-BY-4.0.

Limitations

• The dataset is very small (1.3 KB), suggesting it contains only summary results, not the underlying simulation data.
• Column-level documentation is absent; field semantics must be inferred from the description or supplementary materials.
• Row count for the underlying data is unknown, which may limit suitability assessment.

Provenance

Source

Cyril Geismar via figshare.

Collection Method

Results from a linear regression model applied to simulated forest epidemic data.

Freshness

Last updated 2026-06-01 17:54:23; freshness should be verified.