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In anatomy, a heterodont (from Greek, meaning 'different teeth') is an animal which possesses more than a single tooth morphology. [2] [3] Human dentition is heterodont and diphyodont as an example. [4] In vertebrates, heterodont pertains to animals where teeth are differentiated into different forms.
Since the origin of teeth some 450 mya, the vertebrate dentition has diversified within the reptiles, amphibians, and fish: however most of these groups continue to possess a long row of pointed or sharp-sided, undifferentiated teeth (homodont) that are completely replaceable. The mammalian pattern is significantly different.
Sexual dimorphism describes the morphological, physiological, and behavioral differences between males and females of the same species. Most primates are sexually dimorphic for different biological characteristics, such as body size, canine tooth size, craniofacial structure, skeletal dimensions, pelage color and markings, and vocalization. [1]
Thus, comparisons between chimpanzees and Homo sapiens could be used to identify major differences. Major characterizing features of Pan troglodytes dental morphology include the presence of peripherally located cusps, thin enamel, and strong facial prognathism.
In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets. The model is initially fit on a training data set, [3] which is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. [4]
If data is a Series, then data['a'] returns all values with the index value of a. However, if data is a DataFrame, then data['a'] returns all values in the column(s) named a. To avoid this ambiguity, Pandas supports the syntax data.loc['a'] as an alternative way to filter using the index.
Overview of a data-modeling context: Data model is based on Data, Data relationship, Data semantic and Data constraint. A data model provides the details of information to be stored, and is of primary use when the final product is the generation of computer software code for an application or the preparation of a functional specification to aid a computer software make-or-buy decision.
Although small to medium differences between low- and high-fidelity data are sometimes able to be overcome by multifidelity models, large differences (e.g., in KL divergence between novice and expert action distributions) can be problematic leading to decreased predictive performance when compared to models that exclusively relied on high ...