21 research outputs found

    Courtesy and Idleness: Gender Differences in Team Work and Team Competition

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    Does gender play a role in the context of team work? Our results based on a real-effort experiment suggest that performance depends on the composition of the team. We find that female and male performance differ most in mixed teams with revenue sharing between the team members, as men put in significantly more effort than women. The data also indicate that women perform best when competing in pure female teams against male teams whereas men perform best when women are present or in a competitive environment

    Courtesy and Idleness: Gender Differences in Team Work and Team Competition

    Get PDF
    Does gender play a role in the context of team work? Our results based on a real-effort experiment suggest that performance depends on the composition of the team. We find that female and male performance differ most in mixed teams with revenue sharing between the team members, as men put in significantly more effort than women. The data also indicate that women perform best when competing in pure female teams against male teams whereas men perform best when women are present or in a competitive environment.team incentives; gender; tournaments

    Courtesy and Idleness: Gender Differences in Team Work and Team Competition

    Get PDF
    Does gender play a role in the context of team work? Our results based on a real-effort experiment suggest that performance depends on the composition of the team. We find that female and male performance differ most in mixed teams with revenue sharing between the team members, as men put in significantly more effort than women. The data also indicate that women perform best when competing in pure female teams against male teams whereas men perform best when women are present or in a competitive environment.team incentives, gender, tournaments

    Cirrus Cloud Microphysics over Darwin, Australia

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    Ice clouds, crucial to the understanding of both short - and long - term climate trends, are poorly represented in global climate models (GCMs). Cirrus clouds, one of the largest uncertainties in the global radiation budget, have been inadequately studied at low latitudes. Parameterizations exist for mid - latitude and tropical cirrus ( Ivanova et al. 2001; McFarquhar et al. 1997). Due to climate sensitivity in the GCM with respect to cloud input, without robust parameterizations of cirrus clouds, the GCM is inaccurate over most output fields, including radiative forcing, temperature, albedo, and heat flux (Yao and Del Genio 1999). Studies of the microphysical properties of tropical cirrus clouds may result in improved parameterizations for GCMs. Until ten years ago, there were no truly realistic cirrus clouds parameterizations for the different regions of the world in the global climate models (GCMs). A GCM requires information about ice particle diameter/maximum dimension (D), ice water content (IWC), and size distribution (SD) for small and large mode crystals . This study uses the latest tropical Atmospheric Radiative Measurements (ARM) data to analyze the small and large crystals in cirrus clouds over Darwin, Australia

    Campus-based Training in Airborne Atmospheric Research

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    Embry-Riddle Aeronautical University (ERAU) campus in Prescott, Arizona will conduct an aircraft measurement program during Spring Semester 2014 to introduce students in meteorology, aeronautical sciences and other departments to airborne scientific research technology and research flight logistics. The ERAU Department of Meteorology has support from the National Science Foundation (NSF) Division of Atmospheric and Geospace Sciences for an educational deployment of the University of Wyoming King Air (UWKA)

    Aircraft Icing Potential and Ice- and Mixed-phase Cloud Microphysics

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    AbstractCold cloud interactions with aircrafts that fly through them require knowledge of cloud microphysics. Aircrafts must be designed to fly into supercooled clouds, or they must avoid those clouds in order to prevent problems associated with airframe and engine icing. De-icing or anti-icing systems must be engineered to withstand reasonable extremes in terms of ice water content (IWC), supercooled liquid water content (LWC), ice particle size distributions (SDs), and temperature. The aircraft design or certification envelopes (FAR 25, Appendix C; Federal Aviation Administration, 1999) were developed before the advent of modern cloud physics instrumentation. In the case of ice and mixed-phase clouds, data from the aircraft measurements during recent field campaign suggest that cloud temperature is one of the main parameters governing cloud microstructure, the size distributions, and the current icing potential (CIP). This study may help improve airplane icing prediction through better understanding of the ice microphysical properties

    Use of Research Aircraft Data to Validate Mesoscale Model Forecasts

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    A NSF funded Student Training in Airborne Research and Technology (START) two-week deployment of the University of Wyoming King Air (UWKA) research aircraft was conducted at Embry-Riddle Aeronautical University (ERAU) in Prescott, Arizona during late March and early April 2014. Some of the goals of this program were to build knowledge on airborne atmospheric research for undergraduate students across multiple departments and to collect a valuable set of aircraft data for atmospheric model validation. Data collection for 10 research flights is available for mesoscale model case study validation. This project utilizes the Weather Research and Forecasting mesoscale model (WRF), version 3.6.1 Advanced Research WRF (ARW) to simulate the general features of the boundary layer thermodynamic profiles, winds and cloud structure prior to and during the days of selected research flights. Data assimilation and two-way nesting procedures are executed. A fine-grid resolution used for this study is 10 km, while the coarse grid resolution is 30 km. This study investigates case studies of cross-wind and cloud microphysical conditions which limit ERAU pilot training operations, and provide insight on the potential value of implementing the WRF model at ERAU with specialized forecast products that support the ERAU pilot training program

    TRY plant trait database – enhanced coverage and open access

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    Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives
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