An IDB shows data that has been derived from the IDB. Fate IDB files (.fid) are produced when the data is integrated into another data server or verified. The filename format is either [name].[identifier], or [name].[years].identifier (e.g., "IDB_landuse_cs1.6_cc_post1800.fid"). IDBs can be generated with additional information, such as specific locations of selected data for reference and particular event timeframes. IDBs are a useful method of providing extensive data and metadata from a single search (e.g., from a natural science data archive) as well as documenting that the data are derived from an IDB.
A single artificial intelligence learning algorithm can be applied to a large data set in order to generate models with high predictive accuracy (25). The nearest neighbor algorithm is ideally suited for large data sets because, once trained on sufficient data, it can predict the outcome of a new data point, without requiring the data set to be sorted. Here, we demonstrate that when latitude, longitude, and date are the primary predictors of the probability of precipitation over a given day, and especially after removing data recorded for political events from some medium-size cities, a nearest neighbor algorithm can respond with a 99% prediction accuracy.
While the Antarctic cavitation over the last 900 years  has been modeled with a mean rate of about 0.07 mm/year [1937, 26], these data are based on ice core measurements and do not cover the present warm climate. On its own, such historical data cannot be considered representative of the current situation or used to assess the future risk of cavitation under global warming conditions [19 37,24]. d2c66b5586