Seasonal Occurrence of Pine Root Collar Weevil, \u3ci\u3eHylobius Radicis\u3c/i\u3e (Coleoptera: Curculionidae), in Red Pine Stands Undergoing Decline

A trapping scheme was devised for sampling the pine root collar weevil, Hylobius radicis, in mature red pine plantations in Wisconsin. Adult weevils were trapped throughout the 1986 field season, and the method appears sensitive enough to discern temporal and spatial trends. The number of weevils caught was higher in stands symptomatic of the general condition currently labelled Red Pine Decline and Mortality. In some stands there was a strong tendency for trap catches to be particularly high near certain trees. Seasonal trends and sex ratios were compared with published reports of H. radicis activity in Michigan

Detection of Dispersed Radio Pulses: A machine learning approach to candidate identification and classification

Searching for extraterrestrial, transient signals in astronomical data sets is an active area of current research. However, machine learning techniques are lacking in the literature concerning single-pulse detection. This paper presents a new, two-stage approach for identifying and classifying dispersed pulse groups (DPGs) in single-pulse search output. The first stage identified DPGs and extracted features to characterize them using a new peak identification algorithm which tracks sloping tendencies around local maxima in plots of signal-to-noise ratio vs. dispersion measure. The second stage used supervised machine learning to classify DPGs. We created four benchmark data sets: one unbalanced and three balanced versions using three different imbalance treatments.We empirically evaluated 48 classifiers by training and testing binary and multiclass versions of six machine learning algorithms on each of the four benchmark versions. While each classifier had advantages and disadvantages, all classifiers with imbalance treatments had higher recall values than those with unbalanced data, regardless of the machine learning algorithm used. Based on the benchmarking results, we selected a subset of classifiers to classify the full, unlabelled data set of over 1.5 million DPGs identified in 42,405 observations made by the Green Bank Telescope. Overall, the classifiers using a multiclass ensemble tree learner in combination with two oversampling imbalance treatments were the most efficient; they identified additional known pulsars not in the benchmark data set and provided six potential discoveries, with significantly less false positives than the other classifiers.Comment: 13 pages, accepted for publication in MNRAS, ref. MN-15-1713-MJ.R