Now, have a look at more examples from other academic disciplines and notice the coverage of **Oxford 3000 (core vocabulary)** in academic texts. The texts below come from different sections of academic journal articles.

This page is only for demonstration, but if you want you can read any of the texts and try to identify a few examples of **core vocabulary **before you reveal the answer.

Text adapted from Parasie & Dagiral (2013)

**80%** of the words are in the Oxford 3000 list.

Text adapted from Lin & Viswanathan (2016)

**80%** of the words are in the Oxford 3000 list.

Text adapted from Farrell, Owens & McDevitt (2013)

**76%** of the words are in the Oxford 3000 list.

Once we obtain G[ν] , given a wind velocity vector u , we can calculate a theoretical R[ν] according to (33). By applying the DFT to R[ν] following (41) in Appendix A, we obtain a theoretical spectrum curve R[κ] . Let Robs[κ] be a spectrum calculated by (41) or (46) from observed data. By comparing R[κ] and Robs[κ] , we can evaluate how close the theoretical spectrum is to the one observed. One of the most popular ways of evaluating the goodness of fit of R[κ] is the least mean squared (LMS) method in which the squared sum of the residue between the two is evaluated.

Another evaluation method is the maximum likelihood (ML), which pursues the maximization of the likelihood as a function R[κ] with respect to Robs[κ] , considering every R[κ] at an integer κ as a scalar random variable that follows a class of gamma distribution, which a sum of squares of Gaussian random variables obey (often mentioned as “χ2 distribution” in a not so rigorous situation). The ML technique is detailed, for example, by Rice [14]. In this article, we employ the ML method to demonstrate our proposed technique.

Text adapted from Nishimura et al. (2020)

**76%** of the words are in the Oxford 3000 list.

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