The phrase "attribute distribution" is correct and usable in written English.
It can be used in contexts such as statistics, data analysis, or machine learning when discussing how certain attributes are spread across a dataset.
Example: "The attribute distribution of the dataset reveals that most entries fall within the lower range of values."
Alternatives: "characteristic spread" or "feature distribution".
Many of the global attribute distributions are unsurprising and follow expected geographic distributions.
To explore asymmetries between attribute distributions according to gender, we first identify all meta-data attributes present in the dataset.
Terrain attribute distributions differ significantly among study sites, with regional terrain attributes (upslope contributing area, topographic wetness index) being more similar among fields than local terrain attributes (slope gradient, slope curvature).
While the algorithm stays the same, the player can randomize repeatedly for new character attribute distributions until satisfied.
Furthermore, it can be applied to various engineering problems including water irrigation systems, traffic networks, and building automation systems since it deals with attributed distribution and resource allocation in large scale distributed systems.
Despite inherent biases in the data due to source (e.g., geotagged images uploaded to social media sites), we observed that the ethnicity attributes distribution seem to follow expected patterns.
Despite the existence of some skewness in multi-attribute distributions, the normal distribution is symmetric with zero skewness.
Nevertheless to be authoritative, future environments must link nomenclators (like ZooBank, IPNI and Index Fungorum, Mycobank), taxonomic compendia (such as CoL and WoRMS), other classifications (a classification bank, perhaps), literature sources (describing species, their attributes, distributions, and common names), and phylogenies, covering the whole spectrum of biodiversity complexity.
Here, the conserved transcriptome formed an integrated cardiogenic network (Figure 4A) that exhibited mathematical property distributions attributed to scale-free (Figure 4B), and hierarchical (Figure 4C) network topologies, consistent with non-stochastic physiological systems.
We employed network biology analyses and determined that ARACNE multiplicative network (5,147 nodes and 38,610 edges), with threshold of 0.8 exhibits properties of a "true" network as it possesses the scale-freeness attribute (degree distribution follows a power law).
