Ouvir é melhor do que falar? Para o aprendizado, muitas vezes, isso é uma grande verdade. E compartilho um ótimo texto abaixo: Extraído de: O USO DO SILÊNCIO COMO FORMA DE APRENDER Por Prof José Re…

I don’t need to tell you that the last several months have seen an increasing number of climate-related disasters, including severe heatwaves throughout Europe and droughts that are impacting water su...

Namibia has emerged as a critical global stronghold for the southern giraffe, Giraffa giraffa, hosting a steadily growing population that reflects the country's progressive approach to wildlife management. While giraffe numbers are declining elsewhere on the continent, Namibia's population is growing, thanks to conservation, community conservancies, and protection efforts. To share more on this success story and what it means for conservation, we are joined by Dr. Julian Fennessy, GCF Director of Conservation and Audi Ekandjo, the GCF Program Officer for Northwest Namibia.

Generates generalized covariate field (GCF) variables from spatial covariates observed at projected coordinates, and selects a stable subset of them for geospatial prediction. For each input covariate the method builds spatial-pattern features (local indicator of spatial association, local Geary's c, log local variance, rank quantile entropy, geocomplexity, log scale variance, local variogram exponent, and signed z-score and median absolute deviation outlier strengths over a series of buffer radii) and neighbourhood-distribution features (buffer-wise quantiles of the covariate values surrounding each location), reduces the buffer and quantile sweeps to a compact set of interpretable functional summaries, and selects variables by random forest importance combined with spatial-block stability resampling and group voting. The GCF method is positioned as prediction-oriented feature construction: its output feeds any downstream regression learner. Methods are described in Song (2026) &lt;<a href="https://doi.org/10.1080%2F13658816.2026.2729719" target="_top">doi:10.1080/13658816.2026.2729719</a>&gt;.

Generates generalized covariate field (GCF) variables from spatial covariates observed at projected coordinates, and selects a stable subset of them for geospatial prediction. For each input covariate the method builds spatial-pattern features (local indicator of spatial association, local Geary's c, log local variance, rank quantile entropy, geocomplexity, log scale variance, local variogram exponent, and signed z-score and median absolute deviation outlier strengths over a series of buffer radii) and neighbourhood-distribution features (buffer-wise quantiles of the covariate values surrounding each location), reduces the buffer and quantile sweeps to a compact set of interpretable functional summaries, and selects variables by random forest importance combined with spatial-block stability resampling and group voting. The GCF method is positioned as prediction-oriented feature construction: its output feeds any downstream regression learner. Methods are described in Song (2026) &lt;<a href="https://doi.org/10.1080%2F13658816.2026.2729719" target="_top">doi:10.1080/13658816.2026.2729719</a>&gt;.

GCF and MSSA Sign MoU to Advance NTN Certification and Interoperability