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Oysters are described as estuarine ecosystem engineers because their reef structures provide habitat for a variety of flora and fauna, alter hydrodynamics, and affect sediment composition. To what spatial extent oyster reefs influence surrounding infauna and sediment composition remains uncertain. We sampled sediment and infauna across 8 intertidal mudflats at distances up to 100 m from oyster reefs within coastal bays of Virginia, USA, to determine if distance from reefs and physical site characteristics (reef elevation, local hydrodynamics, and oyster cover) explain the spatial distributions of infauna and sediment. Total infauna density increased with distance away from reefs; however, the opposite was observed for predatory crustaceans (primarily crabs). Our results indicate a halo surrounding the reefs of approximately 40 m (using an increase in ~25% of observance as the halo criterion). At 90 m from reefs, bivalves and gastropods were 70% more likely to be found (probability of observance), while there was an approximate 4-fold decrease for large crustaceans compared to locations adjacent to reefs. Increases in percent oyster reef cover and/or mean reef area did not statistically alter infauna densities but showed a statistical correlation with smaller sediment grain size, increased organic matter, and reduced flow rates. Weaker flow conditions within the surrounding mudflats were also associated with smaller grain sizes and higher organic matter content, suggesting multiple drivers on the spatial distribution of sediment composition. This study emphasizes the complexity of bio-physical couplings and the considerable spatial extent over which oyster reefs engineer intertidal communities. 

Habitat suitability models have been used for decades to develop spatially explicit predictions of landscape capacity to support populations of target species. As high-resolution remote sensing data are increasingly included in habitat suitability models that inform spatial conservation and restoration decisions, it is essential to validate model predictions with independent, quantitative data collected over sustained time frames. Here, we used data collected from 12 reefs over a 14 yr sampling period to validate a recently developed physical habitat suitability model for intertidal oyster reefs in coastal Virginia, USA. The model used intertidal elevation, water residence time, and fetch to predict the likelihood of suitable conditions for eastern oysters Crassostrea virginica across a coastal landscape, and remotely sensed elevation was the most restrictive parameter in the model. Model validation revealed that adult oyster biomass was on average 1.5 times greater on oyster reefs located in predicted ‘suitable’ habitat relative to reefs located in predicted ‘less suitable’ habitat over the 14 yr sampling period. By validating this model with long-term population data, we highlight the importance of elevation as a driver of sustained intertidal oyster success. These findings extend the validation of habitat suitability models by quantitatively supporting the inclusion of remotely sensed data in habitat suitability models for intertidal species. Our results suggest that future oyster restoration and aquaculture projects could enhance oyster biomass by using habitat suitability models to select optimal site locations. 

Marsh habitats, experiencing accelerated change, require accurate monitoring techniques. We developed methods to quantify marsh edge morphology using airborne LiDAR data. We then applied these methods within the context of oyster reef restoration within the shallow coastal bays of Virginia, USA, by comparing retreat and morphology quantified at paired reef-lined and control marsh edges at 10 different marsh sites. Retreat metrics were analyzed between 2002 and 2015, utilizing a LiDAR derived edge for the year 2015 from points of maximum slope and aerial imagery pre-2015. Retreat was also compared before and after oyster reef restoration to determine if reefs slow erosion. We found that slope statistics from airborne LiDAR elevation data can accurately capture marsh edge morphology. Retreat rate, measured at edges typically found near the vegetation line, was not significantly different between reef-lined and control marshes and ranged from 0.14 to 0.79 m yr -1 . Both retreat rate (ρ = -0.90) and net movement (ρ = -0.88) were strongly correlated to marsh edge elevation. Exposed control marshes had significantly greater mean and maximum slope values compared to reef-lined marshes. The mean edge slope was 11.4° for exposed marshes and 6.0° for reef-lined marshes. We hypothesize that oyster reefs are causing an elongation of the marsh edge by reducing retreat at lower elevations of the marsh edge. Therefore, changes in marsh edge morphology may be a precursor to changes in marsh retreat rates over longer timescales and emphasizes the need for repeated LiDAR measurements to capture processes driving marsh edge dynamics. 

Oyster populations within the coastal bays of Virginia have greatly declined, mainly due to overharvesting and disease, and past restoration efforts have largely focused on increasing their populations. Current restoration goals have now expanded to simultaneously procure the wider ecosystem services oysters can offer, including shoreline protection and ecosystem diversification. However, tradeoffs exist in designing artificial reefs because it is unlikely one design will optimize all services. This study compares the services provided by reef designs varying in elevation and width located adjacent to an intertidal marsh within a coastal bay of VA, USA. We quantified wave attenuation to determine potential coastal protection of the adjacent marsh, and changes to sediment composition and infaunal communities before and after reef construction for 3 years. After construction, we also quantified oyster size and population density to compare high and low elevation reef designs. High elevation reefs were more effective at attenuating waves and fostering oyster growth compared to low elevation reefs. Oysters atop high elevation reefs were on average approximately twice as dense and 20% larger than those on low elevation designs. Reef width had a minimal effect on oyster population density; densities on high and low reefs were similar for designs with one or three rows. The presence of oyster reefs also increased infaunal diversity and sediment organic matter. Our results indicate that artificial reef design can differentially affect the services provided through restoration, and elevation is especially important to consider when designing for oyster population enhancement and coastal protection. 

A<sc>bstract</sc> A measurement is performed of Higgs bosons produced with high transverse momentum (p_T) via vector boson or gluon fusion in proton-proton collisions. The result is based on a data set with a center-of-mass energy of 13 TeV collected in 2016–2018 with the CMS detector at the LHC and corresponds to an integrated luminosity of 138 fb⁻¹. The decay of a high-p_THiggs boson to a boosted bottom quark-antiquark pair is selected using large-radius jets and employing jet substructure and heavy-flavor taggers based on machine learning techniques. Independent regions targeting the vector boson and gluon fusion mechanisms are defined based on the topology of two quark-initiated jets with large pseudorapidity separation. The signal strengths for both processes are extracted simultaneously by performing a maximum likelihood fit to data in the large-radius jet mass distribution. The observed signal strengths relative to the standard model expectation are$$ {4.9}_{-1.6}^{+1.9} $$ ${4.9}_{-1.6}^{+1.9}$and$$ {1.6}_{-1.5}^{+1.7} $$ ${1.6}_{-1.5}^{+1.7}$for the vector boson and gluon fusion mechanisms, respectively. A differential cross section measurement is also reported in the simplified template cross section framework. 

Abstract Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors. 

Abstract A search is reported for charge-parity$$CP$$ $\mathrm{CP}$violation in$${{{\textrm{D}}}^{{0}}} \rightarrow {{\textrm{K}} _{\text {S}}^{{0}}} {{\textrm{K}} _{\text {S}}^{{0}}} $$ ${\text{D}}^0\to {\text{K}}_{\text{S}}^0{\text{K}}_{\text{S}}^0$decays, using data collected in proton–proton collisions at$$\sqrt{s} = 13\,\text {Te}\hspace{-.08em}\text {V} $$ $\sqrt{s}=13\text{Te}\text{V}$recorded by the CMS experiment in 2018. The analysis uses a dedicated data set that corresponds to an integrated luminosity of 41.6$$\,\text {fb}^{-1}$$ ${\text{fb}}^{-1}$, which consists of about 10 billion events containing a pair of b hadrons, nearly all of which decay to charm hadrons. The flavor of the neutral D meson is determined by the pion charge in the reconstructed decays$${{{\textrm{D}}}^{{*+}}} \rightarrow {{{\textrm{D}}}^{{0}}} {{{\mathrm{\uppi }}}^{{+}}} $$ $\text{D}_{}^{\ast +}\to {\text{D}}^0{\pi }^{+}$and$${{{\textrm{D}}}^{{*-}}} \rightarrow {\overline{{\textrm{D}}}^{{0}}} {{{\mathrm{\uppi }}}^{{-}}} $$ $\text{D}_{}^{\ast -}\to {\overline{\text{D}}}^0{\pi }^{-}$. The$$CP$$ $\mathrm{CP}$asymmetry in$${{{\textrm{D}}}^{{0}}} \rightarrow {{\textrm{K}} _{\text {S}}^{{0}}} {{\textrm{K}} _{\text {S}}^{{0}}} $$ ${\text{D}}^0\to {\text{K}}_{\text{S}}^0{\text{K}}_{\text{S}}^0$is measured to be$$A_{CP} ({{\textrm{K}} _{\text {S}}^{{0}}} {{\textrm{K}} _{\text {S}}^{{0}}} ) = (6.2 \pm 3.0 \pm 0.2 \pm 0.8)\%$$ $A_{\mathrm{CP}}\left({\text{K}}_{\text{S}}^0{\text{K}}_{\text{S}}^0\right)=(6.2\pm 3.0\pm 0.2\pm 0.8)\%$, where the three uncertainties represent the statistical uncertainty, the systematic uncertainty, and the uncertainty in the measurement of the$$CP$$ $\mathrm{CP}$asymmetry in the$${{{\textrm{D}}}^{{0}}} \rightarrow {{\textrm{K}} _{\text {S}}^{{0}}} {{{\mathrm{\uppi }}}^{{+}}} {{{\mathrm{\uppi }}}^{{-}}} $$ ${\text{D}}^0\to {\text{K}}_{\text{S}}^0{\pi }^{+}{\pi }^{-}$decay. This is the first$$CP$$ $\mathrm{CP}$asymmetry measurement by CMS in the charm sector as well as the first to utilize a fully hadronic final state. 

Abstract This paper describes theCombinesoftware package used for statistical analyses by the CMS Collaboration. The package, originally designed to perform searches for a Higgs boson and the combined analysis of those searches, has evolved to become the statistical analysis tool presently used in the majority of measurements and searches performed by the CMS Collaboration. It is not specific to the CMS experiment, and this paper is intended to serve as a reference for users outside of the CMS Collaboration, providing an outline of the most salient features and capabilities. Readers are provided with the possibility to runCombineand reproduce examples provided in this paper using a publicly available container image. Since the package is constantly evolving to meet the demands of ever-increasing data sets and analysis sophistication, this paper cannot cover all details ofCombine. However, the online documentation referenced within this paper provides an up-to-date and complete user guide.