[Re] Resampling methods for evaluating classification accuracy of wildlife habitat models

How do random and spatially structured resampling compare on synthetic data with controlled spatial dependence?

The evaluator generates the dependence structure and held-out labels, enforces split separation and independently measures error-estimation bias.

What you could produce

• Versioned protocol, input and environment manifest, and independent per-case comparison table including uncertainty and incomplete cases.

Before you use it

• Python 3.6.4, NumPy 1.14.2, scikit-learn 0.19.1, Matplotlib 2.1.2, pandas 0.22.0 (historical environment listed in the current named-branch README)
• Optional TeX/dvipng figure-rendering dependencies are mentioned; compatibility and availability were not tested

Limits to keep in view

• No research code was executed; no independent scientific verification has been performed.
• The current qualified pilot is self-contained Python 3.13 with a 90-second author deadline. This article's environment has not been qualified for that path.
• Published source metadata and a historical review do not establish compatibility, successful reproduction, operator independence or current scientific correctness.

Source and permission context

Etherington, Thomas R.; Lieske, David J.. [Re] Resampling methods for evaluating classification accuracy of wildlife habitat models. ReScience C 5(1), #4; 10.5281/zenodo.3234524.

Rights need review. Review the scope and upstream conditions before reuse.

Still unresolved

• The associated code's exact license and version-specific third-party notices have not been independently checked.
• Data licenses, permissions, consent restrictions and redistribution conditions have not been independently checked.
• Artifacts are referenced only; PDF/archive contents, dependency locks and bytes have not been inspected or executed.