Relative Dispersion Research Articles

Impact of ageostrophic dynamics on the predictability of Lagrangian trajectories in surface-ocean turbulence

Turbulent flows at the surface of the ocean deviate from geostrophic equilibrium on scales smaller than about 10 km. These scales are associated with important vertical transport of active and passive tracers, and should play a prominent role in the heat transport at climate scales and for plankton dynamics. Measuring velocity fields on such small scales is notoriously difficult but new, high-resolution satellite altimetry is starting to reveal them. However, the satellite-derived velocities essentially represent the geostrophic flow component, and the impact of unresolved ageostrophic motions on particle dispersion needs to be understood to properly characterize transport properties. Here, we investigate ocean fine-scale turbulence using a model that represents some of the processes due to ageostrophic dynamics. We take a Lagrangian approach and focus on the predictability of the particle dynamics, comparing trajectories advected by either the full flow or by its geostrophic component only. Our results indicate that, over long times, relative dispersion is marginally affected after filtering out the ageostrophic component. Nevertheless, advection by the geostrophic-only flow leads to an overestimation of the typical pair-separation rate, and to a bias on trajectories (in terms of displacement from the actual ones), whose importance grows with the Rossby number. We further explore the intensity of the transient particle clustering induced by ageostrophic motions and find that it can be significant, even for small flow compressibility. Indeed, we show that clustering is here due to the interplay between compressibility and persistent flow structures that trap particles, enhancing their aggregation. Published by the American Physical Society 2024

Enhanced Transverse Dispersion in 3D-Printed Logpile Structures: A Comparative Analysis of Stacking Configurations

Three-dimensionally printed logpile structures have demonstrated the tunability of the transverse dispersion behavior, which is relevant in the context of chemical reactor design. The current modeling study aims to further investigate the trade-offs in such structures, extending the range of geometries investigated and addressing the limitations associated with the pseudo-2D nature of previous experiments. The relative transverse dispersion coefficient and pressure drop were determined using computational fluid dynamics simulations in OpenFOAM for structures with different stacking configurations, porosities and scaling of the structures’ unit cell along the secondary transverse axis. The latter could not be varied in previous experiments, but the current results demonstrate that this limitation suppresses vortex shedding in structures with high porosity. These vortices significantly enhance the transverse dispersion. By using a staggered stacking configuration on both transverse axes, an earlier onset of this phenomenon could be realized. Importantly, operation in this regime could be achieved without an equivalent increase in pressure drop, offering a favorable operating trade-off. The findings demonstrate that at low Reynolds numbers, the studied structures consistently outperform randomly packed beds of spheres, highlighting their potential for chemical process intensification.

Assessment of earthquake location uncertainties for the design of local seismic networks

The ability to estimate earthquake source locations, along with the appraisal of relevant uncertainties, is paramount in monitoring both natural and human-induced micro-seismicity. For this purpose, a monitoring network must be designed to minimize the location errors introduced by geometrically unbalanced networks. In this study, we first review different sources of errors relevant to the localization of seismic events, how they propagate through localization algorithms, and their impact on outcomes. We then propose a quantitative method, based on a Monte Carlo approach, to estimate the uncertainty in earthquake locations that is suited to the design, optimization, and assessment of the performance of a local seismic monitoring network. To illustrate the performance of the proposed approach, we analyzed the distribution of the localization uncertainties and their related dispersion for a highly dense grid of theoretical hypocenters in both the horizontal and vertical directions using an actual monitoring network layout. The results expand, quantitatively, the qualitative indications derived from purely geometrical parameters (azimuthal gap (AG)) and classical detectability maps. The proposed method enables the systematic design, optimization, and evaluation of local seismic monitoring networks, enhancing monitoring accuracy in areas proximal to hydrocarbon production, geothermal fields, underground natural gas storage, and other subsurface activities. This approach aids in the accurate estimation of earthquake source locations and their associated uncertainties, which are crucial for assessing and mitigating seismic risks, thereby enabling the implementation of proactive measures to minimize potential hazards. From an operational perspective, reliably estimating location accuracy is crucial for evaluating the position of seismogenic sources and assessing possible links between well activities and the onset of seismicity.

ANALYSIS OF AGRICULTURAL DEVELOPMENT IN GUANGXI UNDER THE BACKGROUND OF RURAL REVITALIZATION

Chinese economy is undergoing a transformation towards high-quality development. As the foundation of the national economy, agriculture must also adapt to the trend of social and economic development, transform its development mode, and transform towards high-quality agricultural development. The implementation of the rural revitalization strategy must be supported by high-quality agriculture; In the context of rural revitalization, we should focus on the new forms and new connotations of high-quality agricultural development. By establishing an agricultural development quality evaluation index system with 13 indicators from 5 dimensions, including economic benefits, comprehensive production capacity, social services, coordinated development, and sustainable development, we can use the comprehensive index evaluation system to analyze the current situation of agricultural development quality in Guangxi and identify the shortcomings and deficiencies that constrain high-quality agricultural development. The research results of comprehensive indicator evaluation and comparative analysis based on factor analysis method indicate that the overall level of agricultural development quality in Guangxi needs to be improved, with problems such as slow development and improvement speed, common shortcomings that restrict development, relative dispersion of agricultural leading enterprises, and insufficient utilization of agglomeration effect advantages. Based on this, it is proposed to promote the development of agriculture in Guangxi towards branding and intensive scale, cultivate diversified agricultural management entities, improve the agricultural production guarantee system, and accelerate the development of agriculture towards green direction.

Relative dispersion in free-surface turbulence

Relative dispersion in free-surface turbulence

From cell intercalation to flow, the importance of T1 transitions

T1 transitions, also called cell intercalations, are important sources of fluidization of epithelial cell monolayers. We use a multiphase field model to quantify this tissue fluidization in terms of the relative dispersion of cells and characterize the T1 flow profile. We show that the ensemble-averaged flow profile of a T1 transition has a saddle-point pattern accompanied by a localized burst in cell speed. Although the temporal evolution of the relative dispersion of cells depends on specific model details, the different dispersion curves collapse robustly onto a linear function of the number of T1 transitions, implying an important connection between T1 transitions and mixing in dense systems. Published by the American Physical Society 2024

An artificial neural network model for recovering small-scale velocity in large-eddy simulation of isotropic turbulent flows

Small-scale motions in turbulent flows play a significant role in various small-scale processes, such as particle relative dispersion and collision, bubble or droplet deformation, and orientation dynamics of non-sphere particles. Recovering the small-scale flows that cannot be resolved in large eddy simulation (LES) is of great importance for such processes sensitive to the small-scale motions in turbulent flows. This study proposes a subgrid-scale model for recovering the small-scale turbulent velocity field based on the artificial neural network (ANN). The governing equations of small-scale turbulent velocity are linearized, and the pressure gradient and the nonlinear convection term are modeled with the aid of the ANN. Direct numerical simulation (DNS) and filtered direct numerical simulation (FDNS) provide the data required for training and validating the ANN. The large-scale velocity and velocity gradient tensor are selected as inputs for the ANN model. The linearized governing equations of small-scale turbulent velocity are numerically solved by coupling the large-scale flow field information. The results indicate that the model established by the ANN can accurately recover the small-scale velocity lost in FDNS due to filtering operation. With the ANN model, the flow fields at different Reynolds numbers agree well with the DNS results regarding velocity field statistics, flow field structures, turbulent energy spectra, and two-point, two-time Lagrangian correlation functions. This study demonstrates that the proposed ANN model can be applied to recovering the small-scale velocity field in the LES of isotropic turbulent flows at different Reynolds numbers.

Nanoparticle agglomeration in molten salt nanofluids: Free energy landscape and its impacts on thermal transport property degradation

The nanofluids consisting of molten salt and nanoparticles can gain enhanced thermal performance for heat transfer and energy storage processes. Agglomeration of nanoparticles is ubiquitous in this colloidal dispersion, deteriorating the dispersed states of the nanocomposite interiors, and its microscopic mechanisms are unsatisfactorily explored. In this work, molecular dynamics simulations combined with umbrella sampling technique to capture the relative dispersion state of nanoparticles in molten salts effectively were performed to reveal the free energy landscape of agglomeration and its influence on heat transport. The dominant deep minima in the free energy surface demonstrates that the agglomeration of MgO nanoparticles in the molten NaCl salt is principally induced by van der Waals attractions between MgO nanoparticles and aggravated by large surface area. The unstable dispersion of nanoparticles is the most fatal problem for enhanced thermal conductivity during agglomeration, and the relative distance of MgO nanoparticles has less impacts on the thermal conductivity of the melt. The stronger adsorption of ions around nanoparticles can improve their relative dispersion, and utilize interfacial effects to enhance the thermal conductivity. Combined with the benefit of ordered ionic layer on the surface, the MgO nanoparticles of 10 Å diameter (specific surface area of ∼ 938.80 m2/g) become the optimal choice for molten NaCl. With the mechanisms of interfacial microstructures preventing agglomerated nanoparticles and improving heat transfer of molten salts revealed, this work can enlighten the selection or feasible modification for nanoparticles in various high-temperature molten salts, since the mechanisms are universal to other nanofluids, especially for ionic liquid based nanofluids.

Landscape effects on dispersal‐related traits in a sedentary butterfly in agricultural landscapes

Abstract Agricultural intensification causes the loss and fragmentation of natural habitats, which negatively impacts farmland biodiversity. The concomitant isolation of essential resources may favour increased dispersal ability in flying insects, resulting in differences in flight morphology and dispersal propensity across landscapes. We collected the butterfly Coenonympha pamphilus in four replicated landscape pairs, each consisting of (a) a highly fragmented and intensified ‘modern’ and (b) a less fragmented ‘traditional’ agricultural landscape. We compared condition (i.e. storage reserves), flight morphology, dispersal capacity and propensity of the collected butterflies between both landscape types. We also tested for the impact of landscape composition and configuration at different spatial scales on butterfly traits. No differences between landscape types per se could be found, but a variety of landscape features affected butterfly traits, indicating complex interactions between landscape heterogeneity and dispersal. In particular, landscapes with a high proportion of crop fields appeared to be selected for phenotypes enhancing dispersal ability. Since dispersal is a key factor for the persistence of metapopulations in fragmented landscapes, we argue that limitations on dispersal in relation to compositional and configurational landscape heterogeneity should be considered in the debate on insect declines in agricultural landscapes.

Improved understanding of calibration efficiency, difficulty and parameter uniqueness of conceptual rainfall runoff models using fitness landscape metrics

The ease and efficiency with which conceptual rainfall runoff (CRR) models can be calibrated, as well as issues related to the uniqueness of their parameters, has received significant attention in literature. While several studies have tried to gain a better understanding of the underlying factors affecting these issues by examining the features of model error surfaces, this has generally been done in an ad-hoc fashion using lower-dimensional representations of higher-dimensional surfaces. In this paper, it is suggested that exploratory landscape analysis (ELA) metrics can be used to quantify key features of the error surfaces of CRR models, including their roughness and flatness, as well as their degree of optima dispersion throughout the surface. This enables key error surface features of CRR models to be compared in a consistent, efficient and easily communicable fashion for models with different combinations of attributes (e.g. model structure, catchment climate conditions, error metrics, and calibration data set lengths). Results from the application of ELA metrics to the error surfaces of 420 CRR models with different combinations of the above attributes indicate that increasing model complexity results in an increase in relative error surface roughness and relative optima dispersion and that, while increasing catchment wetness increases the relative roughness of error surfaces, it also decreases optima dispersion. This suggests that for the models considered in this study, optimisation efficiency is likely to decrease with increasing model complexity and catchment wetness, while optimisation difficulty is likely to increase and parameter uniqueness likely to decrease with model complexity and catchment dryness. While implications for choice of model complexity will need further work, this study highlights the potential value of the proposed approach to understanding the calibration efficiency, difficulty and parameter uniqueness of conceptual rainfall runoff models.

UP‐GAN: Channel‐spatial attention‐based progressive generative adversarial network for underwater image enhancement

AbstractFocusing on severe color deviation, low brightness, and mixed noise caused by inherent scattering and light attenuation effects within underwater environments, an underwater‐attention progressive generative adversarial network (UP‐GAN) is innovated for underwater image enhancement (UIE). Salient contributions are as follows: (1) By elaborately devising an underwater background light estimation module via an underwater imaging model, the degradation mechanism can be sufficiently integrated to fuse prior information, which in turn saves computational burden on subsequent enhancement; (2) to suppress mixed noise and enhance foreground, simultaneously, an underwater dual‐attention module is created to fertilize skip connection from channel and spatial aspects, thereby getting rid of noise amplification within the UIE; and (3) by systematically combining with spatial consistency, exposure control, color constancy, color relative dispersion losses, the entire UP‐GAN framework is skillfully optimized by taking into account multidegradation factors. Comprehensive experiments conducted on the UIEB data set demonstrate the effectiveness and superiority of the proposed UP‐GAN in terms of both subjective and objective aspects.

Relative dispersion and relative diffusivities in an ocean-wave coupled model of the North Sea

The study analyzes the impact of various wave-induced processes on relative dispersion and diffusivities in the North Sea using OpenDrift, a Lagrangian particle-drift model driven by a fully coupled NEMO-WAM model. The coupled model parameterizations include sea state-dependent momentum flux, energy flux, and wave-induced mixing. The study demonstrates that Eulerian currents, influenced by the interaction between the ocean and wave models, significantly enhance particle transport. Experiments conducted using drifter clusters obtained during an RV Heincke excursion further confirm the impact of wind-wave coupling. The analysis includes a comparison of results from experiments with and without wave coupling. The impact of diffusion in the Lagrangian model on relative dispersion is investigated, with the conclusion that diffusion is essential for achieving precise simulations. Furthermore, the incorporation of wind-wave-driven mixing parameters, including sea state-dependent momentum flux, energy flux, and wave-induced mixing, into the hydrodynamic model leads to elevated levels of relative dispersion and diffusivity.

Inflation, Price Dispersion, and Welfare: The Role of Consumer Search

In standard macroeconomic models, the costs of inflation are tightly linked to the price dispersion of identical goods. Therefore, understanding how price dispersion empirically relates to inflation is crucial for welfare analysis. In this paper, I study the relationship between steady-state inflation and price dispersion for a cross section of U.S. retail products using scanner data. By comparing prices of items with the same barcode, my measure of relative price dispersion controls for product heterogeneity, overcoming an important challenge in the literature. I document a new fact: price dispersion of identical goods increases steeply around zero inflation and becomes flatter as inflation increases, displaying an Upsilon-shaped pattern. Current sticky-price models are inconsistent with this finding. I develop a menu-cost model with idiosyncratic productivity shocks and sequential consumer search that reproduces the new fact and exhibits realistic price-setting behavior. In the model, inflation-induced price dispersion increases shoppers' incentives to search for low prices and thus competition among retailers. The positive welfare-maximizing inflation rate optimally trades off the efficiency gains from lower markups and the resources spent on search.

Mixed response of trace element concentrations in fluvial sediments to a flash flood in a former mining area

BackgroundFloods, especially flash floods, are the major transporting agent for fluvial sediments, whose pollution is a global concern. As floods result in the dispersion of and exposure to these sediments, a profound understanding of sedimentary dynamics during flood events and the related pollutant dispersion is of relevance. However, the characteristics of extreme flood events concerning pollutant dynamics are insufficiently known so far.ResultsIn a Central European catchment impacted by intense industrial activities and former mining, over the course of five years, we surveyed six high-discharge events, five of them approx. bankfull discharge and one major flash flood event, supplemented by sampling of bank sediments. Fluvial sediments were analyzed for elemental composition by X-Ray fluorescence and for grain size distribution of the fine faction by laser diffraction. By applying a local enrichment factor, trace metal(loid) signatures in these sample sets were compared. Furthermore, Positive Matrix Factorization was used to investigate the trace metal(loid)s’ sources.The sediments deposited by minor flooding had continual trace metal(loid) signatures. However, for the extreme event, significant divergencies arose and persisted for the following years: The enrichment of anthropogenically influenced elements increased, with a slowly decreasing trend in the subsequent two years. Naturally dominated metal(oid)s decrease in enrichment without indicating a return to original levels. In contrast, other elements were insensitive to the extreme event. Positive Matrix Factorization identified anthropogenic influences in elements originating from copper and lead processing and mining activities. Furthermore, bed sediments and a natural background factor were found to dominate the non-anthropogenically influenced metal(loid)s.ConclusionsIn between extreme events, winnowing processes slowly alter the elemental composition of bed sediments. The depletion of such sediments due to the flash flood proves catchment-wide flushing, which induces a natural resetting of the geochemical signals. This ability to renew is an integral part of resilience in fluvial systems. This mechanism is disturbed by industrial activities in floodplains. The exceptional flooding reaches infrastructure that is assumed to be safe and, therefore, unprotected. These additional sources can shift flood sediments’ trace metal(loid) signature, which has a long-lasting impact on the catchment sediments. However, the modifications depend on the flooding extent, possible emitters, and protection measures.

Relative dispersion ratios following fecal microbiota transplant elucidate principles governing microbial migration dynamics

Microorganisms frequently migrate from one ecosystem to another. Yet, despite the potential importance of this process in modulating the environment and the microbial ecosystem, our understanding of the fundamental forces that govern microbial dispersion is still lacking. Moreover, while theoretical models and in-vitro experiments have highlighted the contribution of species interactions to community assembly, identifying such interactions in vivo, specifically in communities as complex as the human gut, remains challenging. To address this gap, here we introduce a robust and rigorous computational framework, termed Relative Dispersion Ratio (RDR) analysis, and leverage data from well-characterized fecal microbiota transplant trials, to rigorously pinpoint dependencies between taxa during the colonization of human gastrointestinal tract. Our analysis identifies numerous pairwise dependencies between co-colonizing microbes during migration between gastrointestinal environments. We further demonstrate that identified dependencies agree with previously reported findings from in-vitro experiments and population-wide distribution patterns. Finally, we explore metabolic dependencies between these taxa and characterize the functional properties that facilitate effective dispersion. Collectively, our findings provide insights into the principles and determinants of community dynamics following ecological translocation, informing potential opportunities for precise community design.

How do productivity gradient and diffusion shape patterns in a plant–herbivore grazing system?

Natural systems show heterogeneous patchy distributions of vegetation over large landscapes. Reaction–diffusion systems can demonstrate such heterogeneity of species distributions. Here, we analyse a reaction–diffusion model of plant–herbivore interactions in two-dimensional space to illustrate non-homogeneous distributions of plants and herbivores. The non-spatial system shows bottom-up control, where herbivore density is low under low and high primary productivity but increased at intermediate productivity. In addition, the non-spatial system provides bistability between a dense vegetation state devoid of herbivores and a coexisting state of plants and herbivores. In the spatiotemporal model, we give analytical conditions of occurring diffusion-driven (Turing) instability, where a novel point in our model is the relative dispersal of herbivores, which represents the movement of herbivores from a higher to a lower vegetation state in addition to the self-diffusion of both species. It is shown that heterogeneity in the population distribution does not occur if the relative dispersal of herbivores is low, but it appears in the opposite case. Due to bistability in the underlying non-spatial system, the spatiotemporal model produces initial value-dependent patterns. The two initial values make different patterns despite having the same primary productivity and relative dispersal rate. As productivity increases with a given relative herbivore dispersal, pattern transition occurs from a blend of stripes and spots of low vegetation state to a predominantly low-density vegetation state with smaller patches of densely vegetated states with one initial value. On the contrary, a discernible change in vegetation patterns from cold spots in the dense vegetation to hot stripes in the primarily low-vegetated state is noticed under the other initial population value. Furthermore, the population distributions of plants and herbivores in the entire domain after a long period are heterogeneous for both initial values, provided the relative herbivore dispersal is substantial. We estimated mean population densities to observe species fitness in the whole domain under variable productivity. When productivity is high, the mean population density of plants may go up or down, depending on the herbivore’s relative dispersal rate. In contrast to the bottom-up control dynamics of the non-spatial system, the system exhibits a top-down control under high relative dispersal, where the herbivore regulates vegetation growth under high productivity. On the other hand, herbivores are extinct under high productivity if the relative dispersal is low.

Broadband dispersion compensation and high birefringence photonic crystal fiber for CWDM/DWDM networks

In this study, we propose a new design based on photonic crystal fibers (PCFs) for broadband dispersion compensation in telecommunication networks. The proposed design has a hexagonal structure arrangement of air-holes rings of different diameters between the silica core and the cladding. The PCF properties like effective area, nonlinearity, dispersion slope, confinement loss, and birefringence are reported and discussed. For the best performance we present three designs A, B and C. Simulation results show that the three designs cover the six-telecommunication optical bands O-, E-, S-, C-, L- and U- bands (wavelengths ranging from 1260 to 1675 nm). Design A achieves a large negative dispersion value of about − 1716 ps/(nm.km) with relative dispersion slope equals to that of conventional single-mode optical fibers (SMFs) of about 0.0036 nm−1, which makes it very suitable for long-haul DWDM transmission systems. With a little modification in the core, designs B and C achieve much higher confinement ability and achieve a very large birefringence value for polarization mode dispersion and sensing applications. Design C is engineered to have exact opposite dispersion of SMF with zero dispersion at the wavelength 1310 nm, which makes it a promising design in CWDM transmission system. The numerical values have been investigated using the full vector finite element method.

Reduced survival in a soaring bird breeding in wind turbine proximity along the northern Baltic Sea coast

Wind power plays a vital role in global climate action and plans for new turbines in the Baltic Sea region are underway. However, it is crucial not to overlook the environmental impact on wildlife, which can be difficult to quantify, especially in elusive and hard-to-track species. One of these species is the white-tailed eagle, a conservation success story that faces a significant collision risk with rotor blades, particularly as turbines are being constructed in its coastal habitats. To assess the effect of wind turbines on survival of territorial adults, we genotyped DNA from adult feathers collected at nests between 2010 and 2022. By tracking individuals across years, we measured survival and breeding dispersal in relation to wind turbine presence. Turbines within a 5 km radius of nests were found to reduce annual survival rates by 7.6 %, while resighting probability and breeding dispersal probability were unaffected. The proportion of territories exposed to wind turbines is currently low (4–5 %), mainly because recent construction sites have been further inland. However, future projections suggest an increase in territory exposure, indicating potential for population-level risks. This highlights the need for substantial safety buffers around nest sites (preferably >5 km) to protect the breeding white-tailed eagle population. Currently, a comprehensive understanding of the large-scale impact on the Baltic Sea white-tailed eagle population is lacking. As wind power expands, it is necessary to consider its impact on wildlife and we recommend conducting ongoing environmental assessments to monitor and adapt conservation measures.

In-situ observations of cloud microphysics over Arabian Sea during dust transport events

The unique in situ measurements of clouds and precipitation within the shallow and deep cumulus over the north-eastern Arabian Sea region during the Indian monsoon are illustrated in this study with a focus on droplet spectral parameters. The observational period showed a significant incursion of Arabian dust and the presence of giant cloud condensation nuclei (GCCN), modifying the cloud and precipitation spectral properties. Warm rain microphysics supported the mixed-phase development in these clouds and exhibited hydrometeors of snow, graupel and large aggregates as part of ice processes. Cloud base droplet number concentration is about 142 ± 79 cm−3 which is one third of the cloud condensation nuclei (CCN) number concentration at 0.2% supersaturation. A rapid broadening of droplet size distribution (DSD) near to the cloud base was noted in contrast to polluted continental clouds. Relationship between the relative dispersion ( ε; the ratio of DSD spectral width ( σ ) to mean radius ( rm )) and liquid water adiabatic fraction (AF) indicates that the entrainment effect has increased relative dispersion significantly (2–3 times larger) in these clouds. Effective radius ( reff ) is found to be proportional to mean volume radius ( rv ) with a proportionality constant ( β ) that varies between 1.0–1.6, depending on the spectral dispersion parameter. Drop size distributions for the small cloud droplets with size range 2–50 μ m and the large drizzle drops (or ice hydrometeors) with size range 100–6400 μ m are parameterized using the gamma function distributions useful for large-scale cloud models.

The Mass Dependence of Hα Emission and Stellar Spindown for Fully Convective M Dwarfs

Fully convective M dwarfs typically remain rapidly rotating and magnetically active for billions of years, followed by an abrupt and mass-dependent transition to slow rotation and quiescence. A robust understanding of this process is complicated by difficulties in estimating M dwarf ages and potential dependencies on other variables such as birth environment or metallicity. To isolate the effect of mass, we consider M dwarfs in wide binaries. We identify 67 widely separated, fully convective (0.08–0.35 M ⊙) M dwarf binary systems using Gaia and measure the Hα feature for each component. We classify the pairs into three categories: systems where both components are active, systems where both are inactive, and candidate transition systems, where one component is active and the other inactive. We gather higher-resolution spectra of the candidate transition systems to verify that their behavior does not result from an unresolved third component, yielding one new triple with surprising activity levels. Neglecting this triple, we find 22 active, 36 inactive, and eight transition pairs. Our results are consistent with the epoch of spindown for these binaries being primarily determined by mass, with mild second-order effects; we place a 1σ upper limit of 0.5 Gyr or 25% on the dispersion in the mass-dependent spindown relation. Our findings suggest that the large dispersion in the spindown epoch previously observed for field stars of a given mass may stem from differences in birth environment, in addition to modest intrinsic stochasticity. We also see evidence that the wide binary population is dispersed over time due to dynamical processing.