Riparian corridors in California are known to be an important but reduced and degraded resource for landbirds. In spite of previous research, the habitat characteristics that correlate with high landbird abundance remain poorly understood. In particular, the scale at which predictive models are useful (fine scale, watershed, sub-region or region) is ill defined. Herein, point count-based abundance indices for 8 riparian associated/obligate species with uniform and high detection probabilities are correlated with biotic and abiotic habitat variables: a sums of squares procedure is used to select the top 5 predictive variables for each species, best fit linear models are selected in an information theoretic framework, and the relative importance of individual variables assessed. These analyses identified site and vegetation characteristics that could serve as targets for restoration and conservation efforts within this coastal central California region. The specific characteristics vary somewhat across the 8 species I surveyed. In addition, the characteristics that I have found important as predictors are distinct from analyses that others have conducted. Therefore, just as we should probably accept regional variation in the composition of riparian avifaunas, we should also probably expect regional variation in the relationship between habitat variables and avian abundance. It appears that important habitat characteristics vary at the fine, watershed, sub-region and regional scales thus reducing the generality of all of the currently available models.

Denna studie fokuserar på att klassificera hat-meddelanden riktade mot mobiloperatörerna Verizon,  AT&amp;T and Sprint. Huvudsyftet är att med hjälp av maskininlärningsalgoritmen Support Vector Machines (SVM) klassificera meddelanden i fyra kategorier - Hat, Orsak, Explicit och Övrigt - för att kunna identifiera ett hat-meddelande och dess orsak. Studien resulterade i två metoder: en "naiv" metod (the Naive Method, NM) och en mer "avancerad" metod (the Partial Timeline Method, PTM). NM är en binär metod i den bemärkelsen att den ställer frågan: "Tillhör denna tweet klassen Hat?". PTM ställer samma fråga men till en begränsad mängd av tweets, dvs bara de som ligger inom ± 30 min från publiceringen av hat-tweeten. Sammanfattningsvis indikerade studiens resultat att PTM är noggrannare än NM. Dock tar den inte hänsyn till samtliga tweets på användarens tidslinje. Därför medför valet av metod en avvägning: PTM erbjuder en noggrannare klassificering och NM erbjuder en mer utförlig klassificering. / This study focused on finding the hate tweets posted by the customers of three mobileoperators Verizon, AT&amp;T and Sprint and identifying the reasons for their dissatisfaction. The timelines with a hate tweet were collected and studied for the presence of an explanation. A machine learning approach was employed using four categories: Hate, Reason, Explanatory and Other. The classication was conducted with one-versus-all approach using Support Vector Machines algorithm implemented in a LIBSVM tool. The study resulted in two methodologies: the Naive method (NM) and the Partial Time-line Method (PTM). The Naive Method relied only on the feature space consisting of the most representative words chosen with Akaike Information Criterion. PTM utilized the fact that the majority of the explanations were posted within a one-hour time window of the posting of a hate tweet. We found that the accuracy of PTM is higher than for NM. In addition, PTM saves time and memory by analysing fewer tweets. At the same time this implies a trade-off between relevance and completeness. / <p>Opponent: Kristina Wettainen</p>