12/27/2022 0 Comments Asian name pronounced tinctaOverall, the lower species richness, density and biomass in plantations than in rainforest, and the changes in the functional composition of the testate amoebae community, indicate detrimental effects of rainforest conversion on the structure and functioning of microbial food webs. This difference suggests that rainforest conversion changes biogenic silicon pools and increases silicon losses. In addition, the relative density of species with siliceous shells was >50% lower in the litter layer of oil palm and rubber compared to rainforest and jungle rubber. This was particularly so in oil palm plantations. In contrast, plantations had a low density of high trophic level species indicating losses of functions. Similar abundances of species of high and low trophic level in rainforest suggest that trophic interactions are more balanced, with a high number of functionally redundant species, than in rubber and oil palm. Living testate amoebae species richness, density and biomass were all lower in replacement land uses than in rainforest, with the impact being more pronounced in litter than in soil. We investigated the effects of conversion of rainforest into jungle rubber, intensive rubber and oil palm plantations on testate amoebae, diverse and functionally important protists in litter and soil. The code is open-source on GitHub and awaits your modifications.Large areas of tropical rainforest are being converted to agricultural and plantation land uses, but little is known of biodiversity and ecological functioning under these replacement land uses. How can I contribute to this wonderful project? In any case, always check with a Japanese friend before getting any badass tattoos based on this web site. For instance, is Jaime pronounced /'jeɪmi:/ (JAY-mee) or /'haɪmeɪ/ (HIGH-may)? The vowel system is very irregular, and some names are even ambiguous. In my defense, transliteration is not an easy task, especially with a language as orthographically challenged as English. In my own tests, it had an accuracy of about 95% on a per-character basic, but your mileage may vary. The machine learning method sometimes makes mistakes. Hey, doofus, you messed up my name! I'm Daenerys Targaryen, and you got the last vowel wrong!Ĭongratulations, you took high-school Japanese. This blog post gives more details, for those interested in a complete answer. more subtle rules are applied, such as "replace G with J if it's followed by an E." Here is the full list of rules. For instance, the first rule the system learns is to replace the letter "L" with the letter "R", because there is no "L" in Japanese. This method is very similar to the Transformation-Based Learner (TBL) invented by Eric Brill.Įssentially, given a list of English/Japanese name pairs, the system learns a series of substitution rules to apply to the English input in order to get the Japanese output. For other names, a learned substitution model trained on these names is applied instead. The Japanese Name Converter uses a combination of dictionary lookup, substitution rules, and machine learning to convert English characters into katakana.įor common English names, a dictionary lookup of about 4,000 English names is used.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |