177,171 research outputs found
The Effect of Free Agency and Luxury Tax on Competitive Balance in Major League Baseball
No full textSamuel Essery ’25, Quantitative Economics major
Faculty mentor: Dr. Fang Dong, Economics
This study investigates the effects of free agency and the luxury tax on competitive balance in Major League Baseball from 1975 to the present. Using time-series models in R and various panel regressions in Stata, the analysis explores trends in team winning percentages and payroll disparities. The results suggest that while free agency initially widened gaps in team performance, the introduction of the luxury tax helped reduce payroll inequality. Findings provide empirical insight into how league policies have shaped long-term parity
The Effect of Free Agency and Luxury Tax on Competitive Balance in Major League Baseball
No full textSamuel Essery ’25, Quantitative Economics major
Faculty mentor: Dr. Fang Dong, Economics
This study investigates the effects of free agency and the luxury tax on competitive balance in Major League Baseball from 1975 to the present. Using time-series models in R and various panel regressions in Stata, the analysis explores trends in team winning percentages and payroll disparities. The results suggest that while free agency initially widened gaps in team performance, the introduction of the luxury tax helped reduce payroll inequality. Findings provide empirical insight into how league policies have shaped long-term parity
GiuliaMazzotti/FSM2: FSM2 for hyper-resolution forest snow modelling applications
No full textThis model release (FSM2.0.3) includes developments specifically targeting hyper-resolution (meter-scale) forest snow modelling applications, as presented in the following publication: Mazzotti, G., Essery, R., Webster, C., Malle, J., and Jonas, T. 2020. Process-Level Evaluation of a Hyper-Resolution Forest Snow Model Using Distributed Multisensor Observations. Water Resources Research, 56(9), e2020WR027572, https://doi.org/10.1029/2020WR02757
Global Navigation Satellite System (GNSS) survey of Ciste Mhearad snow patch perimeter, Cairngorm, Scotland, 2023
No full textThis dataset contains geographic locations, including the horizontal and vertical position, of the perimeter of the Ciste Mhearad snow patch on Cairngorm for three dates in the summer of 2023. Points on the perimeter were located using two Global Navigation Satellite System (GNSS) receivers as base and roving stations during visits on 19 June, 27 July and 28 July 2023.Essery, R.; Howe, L. (2024). Global Navigation Satellite System (GNSS) survey of Ciste Mhearad snow patch perimeter, Cairngorm, Scotland, 2023. NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/81d05a77-4c95-4f17-bdbf-4c0a0095db6
Implications of spatial distributions of snow mass and melt rate for snow-cover depletion: observations in a subarctic mountain catchment
No full textSpatial statistics of snow water equivalent (SWE) and melt rate were measured using spatially distributed, sequential ground surveys of depth and density in forested, shrub and alpine tundra environments over several seasons within a 185 km(2) mountain catchment in Yukon Territory, Canada. When stratified by slope/aspect sub-units within landscape classes, SWE frequency distributions matched the log-normal, but multiclass surveys showed a more bimodal distribution. Within-class variability of winter SWE could be grouped into (i) windswept tundra and (ii) sheltered tundra/forest regimes. During melt, there was little association between the standard deviation and mean of SWE. At small scales, a negative correlation developed between spatial distributions of pre-melt SWE and melt rate where shrubs were exposed above the snow. This was not evident in dense-forest, alpine-tundra or deep-snowdrift landscape classes. At medium scales, adja-negative SWE and melt-rate correlations were also found between mean values from adjacent slope sub-units of the tundra landscape class. The medium-scale correlation was likely due to slope effects on insolation and blowing-snow redistribution. At the catchment scale, the correlation between mean SWE and melt rate from various landscape classes reversed to a positive one, likely influenced by intercepted and blowing regimes, shrub exposure during melt and adiabatic cooling with elevation rise. Covariance at the catchment scale resulted in a 40% acceleration of snow depletion. These results suggest that the spatial variability and covariability of both SWE and melt rate are scale- and landscape-class-specific and need to be considered in a landscape-stratified manner at the appropriate scale when snow depletion is described and the snowmelt duration predicted.</p
Evaluation of snow cover and depth simulated by a land-surface model using detailed regional snow observations from Austria
No full textAn evaluation is undertaken of the accuracy with which the Joint UK Land Environment Simulator (JULES) can simulate snow cover and depth when driven using data from the Hadley Centre Regional Climate Model. The JULES model provides the facility to diagnose the thermal and hydrological state of the land surface and soil given time-varying inputs of air temperature, wind speed, humidity, shortwave and long-wave radiation, and precipitation. The observed dataset used in this study consists of daily snow depths measurements at 601 climate stations with more than 15 years of observations in the period from January 1976 to December 2000. In this study, the JULES model was driven using two datasets at 25 km horizontal resolution: one produced using the UK Met Office Hadley Centre regional climate model (RCM), HadRM3-P (RCM), the other in which RCM precipitation and air temperature data were replaced with observed values (RCM+PT). The results indicate good agreement between the land-surface model simulations and observations of snow cover at climate stations. The median snow cover accuracy indices for all 601 stations were 89% and 91% for the RCM and the combined RCM+PT driving datasets, respectively, with only a small inter-annual variation. In contrast, the differences between modeled and measured snow depth were much larger. The median values of mean snow depth bias were similar, −0.4 cm for the RCM and −1.2 cm for the RCM+PT, however, the RCM simulation was found to overestimate the observed snow depth at more than 25% of climate stations. The extent to which the results from RCM-driven simulations match observed data is strongly related to the accuracy of the RCM precipitation. The large overestimation has significant impact on the snow mass simulation and the assessment of extreme values in the mountains. We note that even if snow cover can be simulated with a high degree of accuracy, this should not imply a similarly high degree of accuracy in the simulation of snow depth. Model performance was poorest in regions of significant topographic heterogeneity and our findings suggest that the most promising additional model developments should be directed towards computationally-efficient representation of sub-grid topography
GEMS: Generalizable empirical model of snow accumulation and melt (version 1.0)
No full textR script and corresponding files for running the Generalizable Empirical Model of Snow accumulation and melt (Umirbekov, Essery and Müller, 2023). The model leverages the capabilities of machine learning methods and incorporates empirical relationships between daily changes in snow water equivalent (SWE) and precipitation, temperature, and topographic factors to produce estimates of daily SWE. The corresponding files include four variations of the pretrained Support Vector Regression, based on a number of the required inputs. The accompanying two datasets include observations from independent Snowpack Telemetry Network (SNOTEL) and the Snow Model Intercomparing Project (ESM-SnowMIP) stations, which were used to evaluate GEMS performance. File descriptions: "GEMS_v1_script.Rmd" - the main script for running the GEMS model; "SVR_GEMS_7P.rds", "SVR_GEMS_5P.rds", "SVR_GEMS_6P.rds" and "SVR_GEMS_4P.rds" - four variations of the pretrained Support Vector Regression, which is an integral part of GEMS. The four versions differ in terms of number of required input variables; "SnowMIP.csv" - in-situ daily meteorological and snow observations from ten reference sites of the ESM-SnowMIP, used to evaluate GEMS performance. The dataset is based on Ménard et al 2019, and contains aggregated daily estimates of precipitation, temperature (mean, max, min) and is supplemented with site specific heat-load index; "SNOTEL_ext.csv" - in-situ daily meteorological and snow observations from 520 SNOTEL stations from 2013 to 2022, used to evaluate GEMS performance. Original SNOTEL temperature records were corrected for bias in the SNOTEL temperature sensor following equation suggested by Brown et al 2019. The dataset is supplemented with station specific heat-load index. References: Brown, C. R., Domonkos, B., Brosten, T., DeMarco, T., & Rebentisch, A. (2019). Transformation of the SNOTEL Temperature Record – Methodology and Implications. 87th Annual Western Snow Conference. Reno, NV. Ménard, C. B., Essery, R., Barr, A., Bartlett, P., Derry, J., Dumont, M., Fierz, C., Kim, H., Kontu, A., Lejeune, Y., Marks, D., Niwano, M., Raleigh, M., Wang, L., and Wever, N. (2019). Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data, Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019. Umirbekov, A., R. Essery & D. Müller (2024) GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers. Geosci. Model Dev., 17, 911-929, https://doi.org/10.5194/gmd-17-911-2024Umirbekov, A., Essery, R., & Müller, D. (2023). GEMS: Generalizable empirical model of snow accumulation and melt (version 1.0). In Geoscientific Model Development (Vol. 17, Number 2, pp. 911–929). Zenodo. https://doi.org/10.5281/zenodo.1016142
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
GEMS: Generalizable empirical model of snow accumulation and melt (version 1.0)
No full text<p>R script and corresponding files for running the Generalizable Empirical Model of Snow accumulation and melt (Umirbekov, Essery and Müller, 2023). The model leverages the capabilities of machine learning methods and incorporates empirical relationships between daily changes in snow water equivalent (SWE) and precipitation, temperature, and topographic factors to produce estimates of daily SWE.</p>
<p>The corresponding files include four variations of the pretrained Support Vector Regression, based on a number of the required inputs. The accompanying two datasets include observations from independent Snowpack Telemetry Network (SNOTEL) and the Snow Model Intercomparing Project (ESM-SnowMIP) stations, which were used to evaluate GEMS performance.</p>
<p><strong>File descriptions</strong>:</p>
<ul>
<li>"<em>GEMS_v1_script.Rmd"</em> - the main script for running the GEMS model;</li>
<li>"<em>SVR_GEMS_7P.rds</em>", "<em>SVR_GEMS_5P.rds</em>", "<em>SVR_GEMS_6P.rds</em>" and "<em>SVR_GEMS_4P.rds</em>" - four variations of the pretrained Support Vector Regression, which is an integral part of GEMS. The four versions differ in terms of number of required input variables;</li>
<li>"<em>SnowMIP.csv</em>" - in-situ daily meteorological and snow observations from ten reference sites of the ESM-SnowMIP, used to evaluate GEMS performance. The dataset is based on Ménard et al 2019, and contains aggregated daily estimates of precipitation, temperature (mean, max, min) and is supplemented with site specific heat-load index;</li>
<li>"<em>SNOTEL_ext.csv" </em>- in-situ daily meteorological and snow observations from 520 SNOTEL stations from 2013 to 2022, used to evaluate GEMS performance. Original SNOTEL temperature records were corrected for bias in the SNOTEL temperature sensor following equation suggested by Brown et al 2019. The dataset is supplemented with station specific heat-load index.</li>
</ul>
<p>References:</p>
<p>Brown, C. R., Domonkos, B., Brosten, T., DeMarco, T., & Rebentisch, A. (2019). Transformation of the SNOTEL Temperature Record – Methodology and Implications. 87th Annual Western Snow Conference. Reno, NV.</p>
<p>Ménard, C. B., Essery, R., Barr, A., Bartlett, P., Derry, J., Dumont, M., Fierz, C., Kim, H., Kontu, A., Lejeune, Y., Marks, D., Niwano, M., Raleigh, M., Wang, L., and Wever, N. (2019). Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data, Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019.</p>
<p>Umirbekov, A., R. Essery & D. Müller (2024) GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers. <em>Geosci. Model Dev.,</em> 17<strong>,</strong> 911-929, https://doi.org/10.5194/gmd-17-911-2024 </p>
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
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