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    Item type:Publication,
    Large scale summarization using ensemble prompts and in context learning approaches
    (Springer Science and Business Media LLC, 2025-03-25)
    Andrés Leiva-Araos
    ;
    Bady Gana
    ;
    Héctor Allende-Cid
    ;
    José García
    ;
    Manob Jyoti Saikia
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    Item type:Publication,
    Mapping employment dynamics in public agencies with payroll data: A methodological framework with an application to Chile
    (Public Library of Science (PLoS), 2024-12-31)
    HERRERA MARÍN, MAURICIO RENÉ  
    ;
    Daniel Brieba
    ;
    Floris Vermeulen
    <jats:p>This study introduces a novel, replicable methodology for analyzing employment dynamics within public sector agencies, focusing on turnover and staff longevity. The methodology is designed to be generalizable and applicable to diverse national contexts where detailed administrative data is available. Using payroll data from over 325,000 Chilean civil servants (2006—2020), we apply mixed-effects Cox survival models and linear mixed models to examine patterns of employment stability across state agencies. By incorporating Propensity Score Matching, we further enhance the causal interpretation of turnover changes, especially in post-election years. Finally, we introduce two key metrics—Service Frailty and Relative Turnover Difference—to quantify long-term stability and short-term, post-electoral disruptions. Our findings highlight substantial differences in turnover patterns between regular and post-election years, as well as significant inter-agency heterogeneity in turnover and employee longevity, largely driven by latent agency characteristics. While major covariates like contract type and staff rank account for some variation, much of the disparity stems from agency-specific factors. This framework offers precise, cross-nationally comparable benchmarks for understanding public sector employment dynamics. Additionally, the methodology contributes to the literature by providing transparent and scalable tools for analyzing workforce stability across different contexts.</jats:p>
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    Estimation of Yield Response Factor for Each Growth Stage under Local Conditions Using AquaCrop-OS
    (2020)
    Mathias Kuschel-Otárola
    ;
    Niels Schütze
    ;
    Eduardo Holzapfel
    ;
    GODOY FAUNDEZ, ALEX ORIEL  
    ;
    Oleksandr Mialyk
    We propose a methodology to estimate the yield response factor (i.e., the slope of the water-yield function) under local conditions for a given crop, weather, sowing date, and management at each growth stage using AquaCrop-OS. The methodology was applied to three crops (maize, sugar beet, and wheat) and four soil types (clay loam, loam, silty clay loam, and silty loam), considering three levels of bulk density: low, medium, and high. Yields are estimated for different weather and management scenarios using a problem-specific algorithm for optimal irrigation scheduling with limited water supply (GET-OPTIS). Our results show a good agreement between benchmarking (mathematical approach) and benchmark (estimated by AquaCrop-OS) using the Normalised Root Mean Square Error (NRMSE), allowing us to estimate reliable yield response factors ( K y ) under local conditions and to dispose of the typical simple mathematical approach, which estimates the yield reduction as a result of water scarcity at each growth stage.
    Scopus© Citations 10  1