A <sub-article> is an article of any type which is entirely contained within another article.
Article types: Letter, reply, review, short new piece, conference abstracts, full text translations, multiple book reviews, series of correspondences, magazine sections/columns
Both the article and the <sub-article> have their own metadata. The container article will have, at the very least, journal information, issue information, and start and end pages; it may also have a title, author, or other metadata. The contained <sub-article> will have its own, independent metadata, such as authors or a smaller page range, that it may not share with the article that encloses it. The sub-article metadata should be tagged using the <front-stub>. Any metadata not specifically tagged within <front-stub> is inherited from the container article. The metadata in <front-stub> about the contained article should not duplicate metadata about the container article.
The pagination tagged in the <front-stub> must reflect the actual pages on which the individual sub-article appears. This will not always be the same as the parent <article> pagination.
Each abstract is tagged in a separate <sub-article> with <title> of the presentation/paper abstract. The full citation of the abstract, including author/presenter should be captured in the <front-stub> of the <sub-article>.
<sub-article article-type="abstract">
<front-stub>
<title-group>
<article-title>Infectious mononucleosis with an unusual Paul-Bunnell result</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Parratt</surname>
<given-names>D.</given-names>
</name>
<xref ref-type="aff" rid="aff0005"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ho-Yen</surname>
<given-names>D. O.</given-names>
</name>
<xref ref-type="aff" rid="aff0005"/>
</contrib>
<aff id="aff0005">Departments of Bacteriology and Haematology, Ninewells Hospital and Medical School, Dundee</aff>
</contrib-group>
</front-stub>
<body><sec><p>[tagged text omitted]</p></sec></body>
</sub-article>
Multiple book reviews within the same manuscript. Each review is tagged separately using <sub-article>
<sub-article>
<front-stub>
<title-group>
<article-title>Assessing and Improving Prediction and Classification, Theory and Algorithms in C++</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Marzjarani</surname>
<given-names>Morteza</given-names></name><xref ref-type="aff" rid="AF0002"/>
</contrib>
<aff id="AF0002"><institution>Saginaw Valley State University</institution> (Retired)</aff>
</contrib-group>
<fpage>273</fpage>
<lpage>280</lpage>
<product><source>Assessing and Improving Prediction and Classification, Theory and Algorithms in C++</source>, by <person-group person-group-type="author"><string-name><given-names>Timothy</given-names> <surname>Masters</surname></string-name></person-group>, <publisher-name>Apress</publisher-name>, <year>2018</year>, xx + 517 pp., <price>$59.99</price>, ISBN-13 (pbk): <isbn>978-1-4842-3335-8</isbn>, ISBN-13 (electronic): <isbn>978-4842-3336-8</isbn>.</product>
</front-stub>
<body>
<sec id="s0002">
<p>This book offers a detailed presentation on evaluating the performance of prediction and classification models along with techniques for improving the performance of such models. The book consists of 9 chapters. Each chapter covers a number of topics along with the computer codes written in C++. The C++ codes are given online or after presenting each topic. Chapter 1 considers the models which make numeric predictions. Several performance measures including MSE mean absolute error (MAE), <italic>R</italic>-squared, root-mean-square error (RMS) along with a comparison of these criteria are presented in this chapter.</p>
<p>In sum, this is a well written book addressing the numeric and classification prediction performance of a model. Topics are covered with sufficient details and more importantly, the C++ codes for the topics are given, which would help the reader to easily run the codes and get the desired results. Although not essential, some familiarity with C++ is helpful in understanding the material. Also, an introductory course in probability and statistics should be sufficient in comprehending the material covered in the book. Labeling sections chronologically would help the reader to separate topics. The book is a great source for researchers interested in this area.</p>
</sec>
<sig-block>
<sig>Morteza Marzjarani<break/><italic>Saginaw Valley State University (Retired)</italic>
</sig>
</sig-block>
</body>
</sub-article>
<sub-article>
<front-stub>
<title-group>
<article-title>Examples in Parametric Inference with R</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Mallik</surname>
<given-names>Abhirup</given-names></name><xref ref-type="aff" rid="AF0003"/>
</contrib>
<aff id="AF0003"><institution>Bosch Center for Artificial Intelligence</institution></aff>
</contrib-group>
<fpage>273</fpage>
<lpage>280</lpage>
<product><source>Examples in Parametric Inference with R</source>, by <person-group person-group-type="author"><string-name><given-names>UlhasJayram</given-names> <surname>Dixit</surname></string-name></person-group>. <publisher-name>Springer</publisher-name>, <year>2016</year>, Lvii + 423 pp., <price>$89.99</price>, ISBN: <isbn>978-981-10-0888-7</isbn>.</product>
</front-stub>
<body>
<sec id="s0003">
<p><italic>Examples in Parametric Inference with R</italic> is intended for a graduate level course on statistical inference. As the author, Professor Dixit mentions, this book grew out of his over 30 years of teaching experience and notes. This is also evident in the format of the book as effort has been made to include many examples and discussions between important theorems and proofs. So, it feels like being in a lecture while reading this book. The content of this book covers the standard graduate level course in statistical inference. The author has used his teaching experience to explain concepts that he found students would have difficulty understanding. For example, he mentions in the preface that the students have confused the relationship between sufficiency and unbiasedness, so he spent enough examples to clarify topics like these. The following is a chapter-wise review of the contents of this book and possibly a few suggestions by this reviewer.</p>
<p>The author has spent the prerequisites chapter for discussing distribution functions, some standard statistical distributions and derived distributions for common estimators like order statistics and range. In Chapter 1, the author introduces concepts of sufficiency and completeness as well as there are some discussion on minimal sufficiency and Basu’s theorem. Examples in the context of exponential families are helpful here. In Chapter 2, author discusses unbiasedness, establishes its relationships with MSE and variance. A large part of this chapter is devoted to explore differences and connections between unbiasedness and sufficiency. The examples of calculating UMVUE for both exponential and nonexponential family distributions are very helpful. There are a few code examples in R for UMVUE calculations as well.</p>
</sec>
<sig-block>
<sig>Abhirup Mallik<break/><italic>Bosch Center for Artificial Intelligence</italic>
</sig>
</sig-block>
</body>
</sub-article>
Multiple conferences tagging within the same manuscript
<sub-article>
<front-stub>
<title-group>
<article-title>Ex Libris Users of North America (ELUNA) Annual Meeting 2018</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Bearden</surname><given-names>Rebecca</given-names></name>
<xref ref-type="aff" rid="AF0002"/>
<xref ref-type="corresp" rid="AN0002"/>
</contrib>
<aff id="AF0002"><institution>Technical Services Librarian, University of Connecticut</institution>, <city>Hartford</ city>, <state>Connecticut</state></aff>
</contrib-group>
<author-notes>
<corresp id="AN0002">E-mail: <email>[email protected]</email></corresp>
</author-notes>
</front-stub>
<body>
<sec><title>Managing serials in Alma: Prediction patterns, claiming, and more</title>
<p>The purpose of this session was to highlight features in ExLibris’s Alma library service platform that support print serials operations. For example, the University of Connecticut School of Law Thomas J. Meskill Law Library (UCONN law library) adapted its own serials operations within Alma after migrating in January 2016.</p>
<p>With over 1,000 active print serial subscriptions and standing orders, one of the technical services department’s primary concerns was prediction patterns, a functionality in Alma that allows for the creation of serial items in advance of their receipt based on machine-readable cataloging (MARC) format captions and patterns data. Prediction patterns are not required, but they would provide easier receipt, save time, and help manage an already complex serials collection more efficiently. Drawbacks can include the staff time required to learn, configure, and maintain prediction patterns, but ultimately, implementation was worth it. The approach to implementation involved research and documentation, then setting up the prediction patterns and related requirements during receipt of the latest material. Then the remainder of serials titles that did not have prediction patterns were targeted using a spreadsheet checklist over a four-month span. To avoid some complex and repetitive work, out-of-the-box and as well as customized prediction pattern templates were utilized.</p>
<p>More information about this presentation can be found in the ELUNA Document Repository (<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://el-una.org/about/eluna-document-repository/">https://el-una.org/about/eluna-document-repository/</ext-link>)</p></sec></body>
</sub-article>
<sub-article>
<front-stub>
<title-group>
<article-title>Northern Ohio Technical Services Librarians (NOTSL) 2018</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Monaco</surname><given-names>Mike</given-names></name>
<xref ref-type="aff" rid="AF0003"/>
<xref ref-type="corresp" rid="AN0003"/>
</contrib>
<aff id="AF0003"><institution>Coordinator, Cataloging Services, The University of Akron</institution>, <city>Akron</city>, <state>Ohio</state></aff>
</contrib-group>
<author-notes>
<corresp id="AN0003">E-mail: <email>[email protected]</email></corresp>
</author-notes>
</front-stub>
<body>
<sec><title>Journalpalooza: All You Need to Know About Serials but Were Afraid to Ask</title>
<p>On Friday, April 27, 2018, the Northern Ohio Technical Services Librarians (NOTSL) held their spring meeting. NOTSL’s biannual meetings feature programs of interest to technical services librarians, and the theme for the spring 2018 meeting was all things serials. In addition to the brief NOTSL business meeting, there were three presentations. Steve Oberg opened with “A Kaleidoscope of Change: Developments and Trends in the Serials Landscape,” followed by Frank Bove and Sean Kennedy, who presented “Changing Tides and Shifting Sands: Serials and Academic Libraries.” The final session was by Mary Frances Labriola and Monique Mason with “Serials in the Public Library, or as Our Patrons Refer to Them, Magazines.”</p>
</sec>
</body>
</sub-article>