Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle

The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from September 2011 to May 2013 at the University of Kentucky Coldstream Dairy. The HR Tag (SCR Engineers Ltd., Netanya, Israel) automatically collected neck activity and rumination data in 2-h increments. The IceQube (IceRobotics Ltd., South Queensferry, United Kingdom) automatically collected number of steps, lying time, standing time, number of transitions from standing to lying (ly-. ing bouts), and total motion, summed in 15-min increments. IceQube data were summed in 2-h increments to match HR Tag data. All behavioral data were collected for 14 d before the predicted calving date. Retrospective data analysis was performed using mixed linear models to examine behavioral changes by day in the 14 d before calving. Bihourly behavioral differences from baseline values over the 14 d before calving were also evaluated using mixed linear models. Changes in daily rumination time, total motion, lying time, and lying bouts occurred in the 14 d before calving. In the bihourly analysis, extreme values for all behaviors occurred in the final 24 h, indicating that the monitored behaviors may be useful in calving prediction. To determine whether technologies were useful at predicting calving, random forest, linear discriminant analysis, and neural network machine -learning techniques were constructed and implemented using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). These methods were used on variables from each technology and all combined variables from both technologies. A neural network analysis that combined variables from both technologies at the daily level yielded 100.0% sen-sitivity and 86.8% specificity. A neural network analysis that combined variables from both technologies in bihourly increments was used to identify 2-h periods in the 8 h before calving with 82.8% sensitivity and 80.4% specificity. Changes in behavior and machine-learning alerts indicate that commercially marketed behavioral monitors may have calving prediction potential

Correcting the Correction: A Revised Formula to Estimate Partial Correlations between True Scores

Bohrnstedt’s (1969) attempt to derive a formula to compute the partial correlation coefficient and simultaneously correct for attenuation sought to simplify the process of performing each task separately. He suggested that his formula, developed from algebraic and psychometric manipulations of the partial correlation coefficient, produces a corrected partial correlation value. However, an algebraic error exists within his derivations. Consequently, the formula proposed by Bohrnstedt does not appropriately represent the value he intended it to estimate. By correcting the erroneous step and continuing the derivation based upon his proposed procedure, the steps outlined in this paper ultimately produce the formula that Bohrnstedt desired.</jats:p

Correcting the Correction: A Revised Formula to Estimate Partial Correlations between True Scores

Bohrnstedt&rsquo;s (1969) attempt to derive a formula to compute the partial correlation coefficient and simultaneously correct for attenuation sought to simplify the process of performing each task separately. He suggested that his formula, developed from algebraic and psychometric manipulations of the partial correlation coefficient, produces a corrected partial correlation value. However, an algebraic error exists within his derivations. Consequently, the formula proposed by Bohrnstedt does not appropriately represent the value he intended it to estimate. By correcting the erroneous step and continuing the derivation based upon his proposed procedure, the steps outlined in this paper ultimately produce the formula that Bohrnstedt desired

Flying Solidarity as an Introductory Lesson in Social Theory

Although students rely on social solidarity in their everyday lives, they generally fail to acknowledge its existence. An active learning class exercise, conducted within approximately 35 minutes, introduces sociology students to Emile Durkheim’s concept of solidarity and the distinction between its mechanical and organic forms. Some groups of students perform the task of creating and flying paper airplanes under the pretense of organic solidarity, and other groups perform the same task under the pretense of mechanical solidarity. Through reflection on their experiences and discussion with members of other groups, students begin to identify nuances of each type of solidarity. An evaluation of this exercise indicates that those who participate in it can describe the distinction between mechanical and organic solidarity and their respective associations with gemeinschaft and gesellschaft better than those exposed to a traditional lesson on the topic can. </jats:p

Generational Spending Habits

The expectation of familiarity with technologies embedded in millennial lifestyle creates a sort of cultural lag, with older generations struggling to understand and use these technologies. However, older generations’ failure to accept new technologies, at least to some degree, would lead to social stagnation. From a populist standpoint, concerns of both generations deserve attention. Hypotheses contend that those in younger generations feel greater strain when using electronic payments than when using currency and that those in older generations feel greater strain when using currency than when using electronic payments. Data gathered from individuals of various ages and in various socio-economic statuses provided moderate, but not overwhelming, support for this contention. However, other, unanticipated, patterns emerge.</jats:p