University of Portsmouth

About: University of Portsmouth is a education organization based out in Portsmouth, Portsmouth, United Kingdom. It is known for research contribution in the topics: Population & Galaxy. The organization has 5452 authors who have published 14256 publications receiving 424346 citations. The organization is also known as: Portsmouth and Gosport School of Science and Art & Portsmouth and Gosport School of Science and the Arts.

Registered Replication Report: Schooler and Engstler-Schooler (1990)

Abstract: Trying to remember something now typically improves your ability to remember it later. However, after watching a video of a simulated bank robbery, participants who verbally described the robber were 25% worse at identifying the robber in a lineup than were participants who instead listed U.S. states and capitals—this has been termed the “verbal overshadowing” effect (Schooler & Engstler-Schooler, 1990). More recent studies suggested that this effect might be substantially smaller than first reported. Given uncertainty about the effect size, the influence of this finding in the memory literature, and its practical importance for police procedures, we conducted two collections of preregistered direct replications (RRR1 and RRR2) that differed only in the order of the description task and a filler task. In RRR1, when the description task immediately followed the robbery, participants who provided a description were 4% less likely to select the robber than were those in the control condition. In RRR2, when the description was delayed by 20 min, they were 16% less likely to select the robber. These findings reveal a robust verbal overshadowing effect that is strongly influenced by the relative timing of the tasks. The discussion considers further implications of these replications for our understanding of verbal overshadowing.

The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: modelling the clustering and halo occupation distribution of BOSS CMASS galaxies in the Final Data Release

Abstract: We present a study of the clustering and halo occupation distribution of Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxies in the redshift range 0.43 < z < 0.7 drawn from the Final SDSS-III Data Release. We compare the BOSS results with the predictions of a halo abundance matching (HAM) clustering model that assigns galaxies to dark matter haloes selected from the large BigMultiDark N-body simulation of a flat Λ cold dark matter Planck cosmology. We compare the observational data with the simulated ones on a light cone constructed from 20 subsequent outputs of the simulation. Observational effects such as incompleteness, geometry, veto masks and fibre collisions are included in the model, which reproduces within 1σ errors the observed monopole of the two-point correlation function at all relevant scales: from the smallest scales, 0.5 h-1 Mpc, up to scales beyond the baryon acoustic oscillation feature. This model also agrees remarkably well with the BOSS galaxy power spectrum (up to k ~ 1 h Mpc-1), and the three-point correlation function. The quadrupole of the correlation function presents some tensions with observations. We discuss possible causes that can explain this disagreement, including target selection effects. Overall, the standard HAM model describes remarkably well the clustering statistics of the CMASS sample. We compare the stellar-to-halo mass relation for the CMASS sample measured using weak lensing in the Canada-France-Hawaii Telescope Stripe 82 Survey with the prediction of our clustering model, and find a good agreement within 1σ. The BigMD-BOSS light cone including properties of BOSS galaxies and halo properties is made publicly available.

Unsupervised Forward Selection: A Method for Eliminating Redundant Variables

Abstract: An unsupervised learning method is proposed for variable selection and its performance assessed using three typical QSAR data sets. The aims of this procedure are to generate a subset of descriptors from any given data set in which the resultant variables are relevant, redundancy is eliminated, and multicollinearity is reduced. Continuum regression, an algorithm encompassing ordinary least squares regression, regression on principal components, and partial least squares regression, was used to construct models from the selected variables. The variable selection routine is shown to produce simple, robust, and easily interpreted models for the chosen data sets.

A global comparison of surface and free‐air temperatures at high elevations

Abstract: [1] Surface and free-air temperature observations from the period 1948–2002 are compared for 1084 surface locations at high elevations (>500 m) on all continents Mean monthly surface temperatures are obtained from two homogeneity adjusted data sets: Global Historical Climate Network (GHCN) and Climatic Research Unit (CRU) Free-air temperatures are interpolated both vertically and horizontally from the National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis R1 25° grids at given pressure levels The compatibility of surface and free-air observations is assessed by examination of the interannual variability of both surface and free-air temperature anomalies and the surface/free-air temperature difference (ΔT) Correlations between monthly surface and free-air anomalies are high The correlation is influenced by topography, valley bottom sites showing lower values, because of the influence of temporally sporadic boundary layer effects The annual cycle of the derived surface/free-air temperature difference (ΔT) demonstrates physically realistic variability Cluster analysis shows coherent ΔT regimes, which are spatially organized Temporal trends in surface and free-air temperatures and ΔT are examined at each location for 1948–1998 Surface temperatures show stronger, more statistically robust and widespread warming than free-air temperatures Thus ΔT is increasing significantly at the majority of sites (>70%) A sensitivity analysis of trend magnitudes shows some reliance on the time period used ΔT trend variability is dominated by surface trend variability because free-air trends are weak, but it is possible that reanalysis trends are unrealistically small Results are sensitive to topography, with mountaintop sites showing weaker ΔT increases than other sites (although still positive) There is no strong relationship between any trend magnitudes and elevation Since ΔT change is dependent on location, it is clear that temperatures at mountain sites are changing in ways contrasting to free air