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Gray whale migration patterns through the Southern California Bight from multi-year visual and acoustic monitoring
Sightings and acoustic recordings from eastern North Pacific gray whales in the Southern California Bight were analyzed for interannual changes and compared with concurrent environmental measurements during 7 migration seasons (2008−2009 to 2014−2015). Acoustic call counts recorded on an offshore hydrophone were highly variable from year to year. Assuming an average calling rate of 7.5 calls whale–1 d–1, the estimated number of whales migrating by this hydrophone would be <10% of the population within 20 km of the offshore hydrophone in most years. In contrast, the estimated number of gray whales migrating off Santa Barbara and Los Angeles based on visual surveys grew at a greater rate (11% yr−1 and 26% yr−1, respectively) than the population size growth rate (5% yr−1). Over the studied migration seasons it seems an increasing proportion of the population was using the nearshore migration corridor in the Southern California Bight, especially near Los Angeles. This trend could increase the negative anthropogenic impact on this species. Although several large-scale climatic events occurred between 2008 and 2015, neither water temperature in the Southern California Bight nor sea ice timing in the gray whale Arctic feeding area improved generalized additive models of gray whale nearshore sightings or offshore acoustic presence. Over these times, the gray whale migration timing appears to be driven more by their biological clock and instinct than by the extrinsic factors accounted for in the present analysis. Future work should test if other factors influence the gray whale migration over longer timescales
Modeling for text compression
The best schemes for text compression employ large models
to help them predict which characters will come next. The actual
next characters are coded with respect to the prediction, resulting
in compression of information. Models are best formed adaptively,
based on the text seen so far. This paper surveys successful
strategies for adaptive modeling which are suitable for use in
practical text compression systems.
The strategies fall into three main classes: finite-context modeling, in
which the last few characters are used to condition the probability
distribution for the next one; finite-state modeling, in which the
distribution is conditioned by the current state (and which subsumes
finite-context modeling as an important special case); and dictionary
modeling, in which strings of characters are replaced by pointers into an
evolving dictionary. A comparison of different methods on the same sample
texts is included, along with an analysis of future research directions.We are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at [email protected]