[1] Geomagnetic polarity time scales (GPTSs) have been constructed by interpolating between dated marine magnetic anomalies assuming uniformly varying spreading rates. A strategy to obtain an optimal GPTS is to minimize spreading rate fluctuations in many ridge systems; however, this has been possible only for a few spreading centers. We describe here a Monte Carlo sampling method that overcomes this limitation and improves GPTS accuracy by incorporating information on polarity chron durations estimated from astrochronology. The sampling generates a large ensemble of GPTSs that simultaneously agree with radiometric age constraints, minimize the global variation in spreading rates, and fit polarity chron durations estimated by astrochronology. A key feature is the inclusion and propagation of data uncertainties, which weigh how each piece of information affects the resulting time scale. The average of the sampled ensemble gives a reference GPTS, and the variance of the ensemble measures the time scale uncertainty. We apply the method to construct MHTC12, an improved version of the M-sequence GPTS (Late Jurassic-Early Cretaceous, $160-120 Ma). This GPTS minimizes the variation in spreading rates in a global data set of magnetic lineations from the Western Pacific, North Atlantic, and Indian Ocean NW of Australia, and it also accounts for the duration of five polarity chrons established from astrochronology (CM0r through CM3r). This GPTS can be updated by repeating the Monte Carlo sampling with additional data that may become available in the future.Citation: Malinverno, A., J. Hildebrandt, M. Tominaga, and J. E. T. Channell (2012), M-sequence geomagnetic polarity time scale (MHTC12) that steadies global spreading rates and incorporates astrochronology constraints,
Predicting reading time has been a subject of much previous work, focusing on how different words affect human processing, measured by reading time. However, previous work has dealt with a limited number of participants as well as word level only predictions (i.e. predicting the time to read a single word). We seek to extend these works by examining whether or not document level predictions are effective, given additional information such as subject matter, font characteristics, and readability metrics. We perform a novel experiment to examine how different features of text contribute to the time it takes to read, distributing and collecting data from over a thousand participants. We then employ a large number of machine learning methods to predict a user's reading time. We find that despite extensive research showing that word level reading time can be most effectively predicted by neural networks, larger scale text can be easily and most accurately predicted by one factor, the number of words.
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