3 resultados para multiple choice tests

em Aquatic Commons


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A method of identifying the beaks and estimating body weight and mantle length of 18 species of cephalopods from the Pacific Ocean is presented. Twenty specimens were selected from each of the following cephalopod species: Symplectoteuthis oualaniensis, Dosidicus gigas, Ommastrephes bartramii, S. luminosa, Todarodes pacificus, Nototodarus hawaiiensis, Ornithoteuthis volalilis, Hyaloteuthis pelagica, Onychoteuthis banksii, Pterygioteuthis giardi, Abraliopsis affinis, A. felis, Liocranchia reinhardti, Leachia danae, Histioteuthis heteropsis, H. dofleini, Gonalus onyx, and Loligo opalescens. Dimensions measured on the upper and lower beak are converted to ratios and compared individually among the species using an analysis of variance procedure with Tukey's omega and Duncan's multiple range tests. Significant differences (P =0.05) observed among the species' beak ratio means and structural characteristics are used to construct artificial keys for the upper and lower beaks of the 18 species. Upper and lower beak dimensions are used as independent variables in a linear regression model with mantle length and body weight (log transformed). (PDF file contains 56 pages.)

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The author uses clicker technology to incorporate polling and multiple choice question techniques into library instruction classes. Clickers can be used to give a keener understanding of how many students grasp the concepts presented in a specific class session. Typically, a student that aces a definition-type question will fail to answer an application-type question correctly. Immediate, electronic feedback helps to calibrate teaching approaches and gather data about learning outcomes. This presentation will analyze learning outcomes specific to scientific disciplines, and demonstrate the usefulness of clickers to engage and sustain student learning.

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The term “selectivity” refers to the relationship between the size (or age) of a fish and its vulnerability to a given kind of fishing gear. A selectivity schedule, along with other parameters, is normally estimated in the course of fitting a stock assessment model, and the estimated schedule can have a large effect on both the estimate of present stock abundance and the choice of an appropriate harvest rate. The form of the relationship is usually not known and not well determined by the data, and equally good model fits can often be obtained with different plausible specifications of selectivity. Choosing among the model fits and associated abundance estimates in this situation is problematic (Sigler, 1999; Sullivan et al., 19