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For the poor and unemployed there can only be a token order, a reminder to the father that he has children for whom he is responsible, and that the order could be revisited if circumstances change.
For example, the pair {Canonical ordering Canonical ordering transformed} should present mirrored results if a system is able to cater for lexical groundings that do not respect the token order as defined in the concept label (i.e. mapping 'Hypoplasia of the optic nerve' to 'Optic nerve hypoplasia').
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The aim of this study was to design and evaluate a token-order-specific naïve Bayes-based machine learning system (RapTAT) to predict associations between phrases and concepts.
Precision, recall, and F-measure on the i2b2 test set were 92.9%, 85.9%, and 89.2% respectively, using the token-order-specific model.
These results demonstrate the feasibility of rapidly and accurately mapping phrases to a wide range of medical concepts based on a token-order-specific naïve Bayes model and machine learning.
In reality, token lines order proplets contiguously, without any empty positions, solely in accordance with the proplets' arrival.
By the same token, in order to construct or conceptualise a cardinal utility function, one typically appeals to preferences over lotteries.
According to this scheme, a node should hold a token in order to participate in any network operation while its adjacent nodes monitor any misbehavior in data packet forwarding functions.
This will give the results we want, but at the cost of making the account completely circular; diagonalizing cannot be a useful strategy for discovering narrow contents if we must already know the narrow content of a mental token in order to apply the strategy.
Methods: The Stat De-id system attempts to identify the local context of a particular token in order to determine the class of the token.
By the same token, birth order can determine, among other things (e.g. social development, healthy food availability), the child's level of activity (how many playmates the child have) and ability to spent time outdoors.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com