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1. An overview of the hydrology of non‐perennial rivers and streams

   https://doi.org/10.1002/wat2.1504  

   Shanafield, Margaret ; Bourke, Sarah A. ; Zimmer, Margaret A. ; Costigan, Katie H. ( February 2021 , WIREs Water)

   Non‐perennial rivers and streams are ubiquitous on our planet. Although several metrics have been used to statistically group or compare streamflow characteristics, there is currently no widely used definition of how many days or over what reach length surface flow must cease in order to classify a river as non‐perennial. At the same time, the breadth of climate and geographic settings for non‐perennial rivers leads to diversity in their flow regimes, such as how often or how quickly they go dry. These rivers have a rich and expanding body of literature addressing their ecologic and geomorphic features, but are often said to be ignored by hydrologists. Yet there is much we do know about their hydrology in terms of streamflow generation processes, water losses, and variability in flow. We also know that while they are prevalent in arid regions, they occur across all climate types and experience a diverse set of natural and anthropogenic controls on streamflow. Furthermore, measuring and modeling the hydrology of these rivers presents a distinct set of challenges, and there are many research directions, which still require further attention. Therefore, we present an overview of the current understanding, methodologic challenges, knowledge gaps, and research directions for hydrologic understanding of non‐perennial rivers; critical topics in light of both growing global water scarcity and ever‐changing laws and policies that dictate whether and how much environmental protection these rivers receive.

   This article is categorized under:

   Science of Water > Science of Water

    

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2. Building a bridge between adaptive capacity and adaptive potential to understand responses to environmental change

   https://doi.org/10.1111/gcb.15579  

   Seaborn, Travis ; Griffith, David ; Kliskey, Andrew ; Caudill, Christopher C. ( March 2021 , Global Change Biology)

   Adaptive capacity is a topic at the forefront of environmental change research with roots in both social, ecological, and evolutionary science. It is closely related to the evolutionary biology concept of adaptive potential. In this systematic literature review, we: (1) summarize the history of these topics and related fields; (2) assess relationship(s) between the concepts among disciplines and the use of the terms in climate change research, and evaluate methodologies, metrics, taxa biases, and the geographic scale of studies; and (3) provide a synthetic conceptual framework to clarify concepts. Bibliometric analyses revealed the terms have been used most frequently in conservation and evolutionary biology journals, respectively. There has been a greater growth in studies of adaptive potential than adaptive capacity since 2001, but a greater geographical extent of adaptive capacity studies. Few studies include both, and use is often superficial. Our synthesis considers adaptive potential as one process contributing to adaptive capacity of complex systems, notes “sociological” adaptive capacity definitions include actions aimed at desired outcome (i.e., policies) as a system driver whereas “biological” definitions exclude such drivers, and suggests models of adaptive capacity require integration of evolutionary and social–ecological system components.

    

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3. The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study

   https://doi.org/10.1145/3543507.3583354  

   Zhang, Yu ; Jin, Bowen ; Zhu, Qi ; Meng, Yu ; Han, Jiawei ( April 2023 , WWW '23: Proceedings of the ACM Web Conference 2023)

   Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature. Scientific literature tagging is beyond a pure multi-label text classification task because papers on the Web are prevalently accompanied by metadata information such as venues, authors, and references, which may serve as additional signals to infer relevant tags. Although there have been studies making use of metadata in academic paper classification, their focus is often restricted to one or two scientific fields (e.g., computer science and biomedicine) and to one specific model. In this work, we systematically study the effect of metadata on scientific literature tagging across 19 fields. We select three representative multi-label classifiers (i.e., a bag-of-words model, a sequence-based model, and a pre-trained language model) and explore their performance change in scientific literature tagging when metadata are fed to the classifiers as additional features. We observe some ubiquitous patterns of metadata’s effects across all fields (e.g., venues are consistently beneficial to paper tagging in almost all cases), as well as some unique patterns in fields other than computer science and biomedicine, which are not explored in previous studies. 

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4. Assessing the evolution of research topics in a biological field using plant science as an example

   https://doi.org/10.1371/journal.pbio.3002612  

   Shiu, Shin-Han ; Lehti-Shiu, Melissa D ( May 2024 , PLOS Biology)

   Dirnagl, Ulrich (Ed.)

   Scientific advances due to conceptual or technological innovations can be revealed by examining how research topics have evolved. But such topical evolution is difficult to uncover and quantify because of the large body of literature and the need for expert knowledge in a wide range of areas in a field. Using plant biology as an example, we used machine learning and language models to classify plant science citations into topics representing interconnected, evolving subfields. The changes in prevalence of topical records over the last 50 years reflect shifts in major research trends and recent radiation of new topics, as well as turnover of model species and vastly different plant science research trajectories among countries. Our approaches readily summarize the topical diversity and evolution of a scientific field with hundreds of thousands of relevant papers, and they can be applied broadly to other fields.

    

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5. Identifying Individual-, Team-, and Organizational-Level Factors that Affect Team Performance in Complex Domains Based on Recent Literature

   https://doi.org/10.1177/1071181322661213  

   Atweh, J.A. ; Moacdieh, N.M. ; Riggs, S.L. ( September 2022 , Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Working in teams provides several advantages to dynamic, data-driven domains, but can also add a layer of complexity to operations. There have been several reviews on teams and team performance analysis; however, there has been limited work in the last five years that has examined micro- and macro-level factors that affect overall team performance. Previous research has proposed a framework within healthcare characterizing team characteristics into three categories: individual contributions, team processes, and organizational structures. However, it is still unclear how new emerging topics in the team literature fit within this framework. Here we provide more specific definitions of the three categories proposed and conduct a review that builds on this framework by adding topics identified from the current literature. To this end, we carried out a systematic search of the human factors literature to examine the research on team performance across various domains from the past five years centered. We then propose ideas for future research on team performance. 

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