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"Remember the Hierarchy, Judge is at the Top": How Judges' Punitiveness and Stigmatizing Attitudes affect Mental Health Court Outcomes
No full textMental health courts—a type of “specialty court”—were established to curb overcrowding in prisons as well as reduce recidivism rates and drug relapses among the mentally ill. The origin of these courts can be traced to a move away from punitive models toward therapeutic jurisprudence, which emphasizes rehabilitation and therapy. Mental health courts have been shown to be effective at reducing recidivism and relapses—but there is variation across these courts in their success rates. Part of what may account for these differences in outcomes is the attitudes of the judges themselves—particularly their punitiveness and their levels of stigma they express toward the mentally ill. To test this idea, I conducted a content analysis of transcripts from extensive multi-month observations of three different mental health courts in the western U.S., with a particular focus on the punitive and stigmatizing statements of the three respective judges as they interact with participants in the program(s). In addition to the content analysis, I employed simple statistics to compare rates of judicial punitiveness and stigmatizing attitudes across the three courts with the success rates of their respective participants. The findings reveal that greater punitiveness and stigma on the part of the judges appear to be associated with higher rates of negative outcomes (i.e. lower success) for participants in their respective programs. Although findings from this comparison of three courts cannot necessarily be generalized to all mental health courts, they are nonetheless suggestive that these courts are most successful when their appointed judges are less punitive and less apt to express stigmatizing attitudes. Put differently, when the attitudes of judges are consistent with the spirit and intent of therapeutic jurisprudence, mental health courts are more likely to reach their intended goals. What this implies about theory and scholarship is that therapeutic jurisprudence appears to be the optimal approach in mental health courts. Additionally, it adds to the literature showing that the judge plays a critical role in the success of their respective programs and participants. In a similar vein, on a practical level, it implies that having the right judge in place—a judge whose attitudes are consistent with therapeutic jurisprudence—is critical for maximizing the effectiveness of mental health courts
Investigating Snowfall Enhancement through Cloud Seeding: A Microphysical Modeling and Remote Sensing Approach
No full textCloud seeding has been employed as a weather modification strategy to enhance precipitation, particularly in arid and semi-arid regions where water scarcity is a constant concern. Despite its widespread use, the scientific understanding of its effectiveness remains incomplete. Many operational programs rely on limited observational data or simplified assumptions, which can obscure the physical mechanisms behind seeding outcomes. This dissertation aims to bridge that gap by combining high-resolution microphysical modeling with satellite-based remote sensing to evaluate the effects of glaciogenic cloud seeding in the western United States. Through a series of case studies and broader regional analyses, this research significantly deepens our understanding of cloud seeding mechanisms and provides insights for future cloud seeding studies.A central tool in this dissertation is the Snow Growth Model for Rimed Snowfall (SGMR), a model developed to simulate key processes involved in ice crystal development, such as nucleation by silver iodide, vapor deposition, aggregation, and riming. The model was first applied to five cloud seeding events in the Lake Tahoe area. For each event, inputs such as cloud top and base heights, temperatures, liquid water content, and ice water content from MERRA-2 and CERES datasets were used to drive the model. The SGMR provided detailed estimates of snowfall rate, particle size, and ice crystal concentration, allowing for an in-depth comparison between seeded and unseeded scenarios.
Building on this initial analysis, the study was expanded to include 13 cloud seeding events across three distinct regions: Lake Tahoe, Santa Rosa Range, and Ruby Mountains. In these cases, ground-based silver iodide generators were used, and the response of the atmosphere was assessed using a combination of GOES-R satellite data and radar reflectivity mosaics. Key spectral channels from the Advanced Baseline Imager (ABI) were analyzed to extract information about cloud-top temperatures, optical thickness, cloud phase, and water vapor profiles. These data were used not only to track cloud evolution but also to assess whether observable changes in cloud structure and precipitation occurred following seeding.
Results from both modeling and satellite analyses point to a clear conclusion: cloud seeding effectiveness is highly dependent on pre-existing atmospheric conditions. Successful seeding events, those that showed increases in snowfall rates and ice crystal concentrations, typically occurred under conditions of abundant supercooled liquid water, colder cloud tops, and moist mid- to upper-tropospheric layers. These conditions favor ice nucleation and growth processes, allowing the seeded particles to initiate or enhance precipitation. On the other hand, events that lacked these favorable environmental characteristics showed minimal response to seeding, reinforcing the idea that not all clouds are good candidates for weather modification.
Another important outcome of this work is the demonstration of how satellite remote sensing, particularly from geostationary platforms like GOES-R, can be used to support and evaluate cloud seeding operations in near-real time. By monitoring cloud microphysical properties and vertical moisture structure, remote sensing provides a valuable supplement to conventional ground-based measurements and offers a pathway toward more data-driven, adaptive cloud seeding strategies.
In summary, this dissertation contributes to a more nuanced and physically grounded understanding of cloud seeding. By integrating advanced modeling with satellite observation, it provides a framework for identifying optimal seeding conditions and assessing developments with greater certainty. The findings have practical implications for the design and evaluation of weather modification programs and offer a foundation for integrating cloud seeding into broader regional water management and climate adaptation efforts
Advancing Understanding of Atmospheric River Flood Hazards: From Antecedent Conditions to Social Vulnerability
No full textAtmospheric rivers (ARs) play a critical role in the global water cycle, producing extreme precipitation and nearly a quarter of global runoff. In many regions, such as the Western United States (U.S.), ARs are a dominant driver of flood hazards and damages. With climate change, the frequency and intensity of AR-driven floods are projected to increase. However, anticipating the magnitude and impacts of AR-driven flooding remains a major challenge. Standard metrics of AR strength, such as the intensity and duration of atmospheric moisture transport, do not always correspond to the hydrologic response. A critical gap remains in connecting ARs to the underlying land surface and social conditions that determine flood severity, timing, and impact. This dissertation addresses this gap by investigating the physical and social factors that shape AR flood impacts across a range of hydrologic and climate settings. In Chapter 2, I evaluate the role of antecedent soil moisture (ASM) in shaping peak streamflow response during ARs across 122 catchments on the U.S. West Coast. I find that ASM exerts a strong non-linear control on event-scale streamflow, with flood magnitudes more than doubling when ASM exceeds a critical, site-specific threshold. With four out of five AR flood events occurring under wet antecedent conditions, I show that ASM offers the best value for AR flood prediction in catchments with lower hydrologic storage capacity driven by shallower soils, less snowpack, and more clay-rich soils with limited infiltration rates. In Chapter 3, I assess the ability of the AR scale to characterize flood-generating ARs using streamflow observations from 145 catchments in California and central Chile. I demonstrate that the current scale, based solely on atmospheric vapor transport, can underestimate flood risk because it does not account for runoff processes controlled by ASM conditions. I propose a modified AR scale for flood hazards that incorporates antecedent precipitation, which significantly enhances the identification of flood-generating ARs across California and central Chile. In Chapter 4, I evaluate the catchment-scale impacts of a plausible, long-duration flooding event simulated from a sequence of 30 days of back-to-back ARs using future climate projections. Using a series of hydrologic and hydraulic models, I present a framework for evaluating how AR flood hazard and exposure evolve. Furthermore, I show that flood duration, not just peak magnitude, drives impacts and exacerbates exposure inequality in areas often overlooked by traditional flood planning. Together, these chapters contribute a new understanding of how integrating land surface and social vulnerability conditions can enhance our knowledge of AR flood hazards.
Innovative Draindown Modeling for Heap Leach Systems Using Hydrus 1D and Python
No full textThis paper was presented at the Heap Leach Solutions Conference, October 19-21, 2025, Sparks, Nevada.Accurate draindown modelling is essential for the design, operation, and closure of heap leach facilities, particularly in scenarios involving complex geometries or challenging operational conditions. Traditional 2D and 3D numerical models, while robust, often require significant computational resources and time, limiting their practicality for iterative design processes or real-time decision-making. To address these challenges, we present an innovative tool that integrates Hydrus 1D with Python to enable automated, large-scale iterative simulations for a pseudo-3D modelling approach. This tool combines the computational efficiency of Hydrus 1D, a widely used software for simulating water flow and solute transport in variably saturated media, with Python scripting to automate the execution of multiple 1D simulations. By discretizing in time, the heap leach facility into a network of vertical 1D columns, the tool captures the time-dependent spatial variability in material properties, stacking sequences, and geometries, providing a comprehensive time-dependent 3D representation of the system without the computational burden of full 2D or 3D models. The methodology is demonstrated through two case studies. The first case study focuses on a conceptual heap leach design with simplified geometry but a challenging stacking sequence. The objective is to predict heap leach outflow rates during operation and closure to support leachate management decisions. The second case study involves an operational heap leach facility situated in mountainous terrain with highly complex geometry. Here, the tool is used to predict the draindown time required for the outflow rate post-closure to reach the evaporation rate of the outflow pond. The results from both case studies highlight the tool's ability to provide actionable insights for both operational and closure planning. Key advantages of this time-dependent pseudo-3D approach include reduced modelling time, enhanced flexibility in scenario analysis, and the ability to incorporate site-specific complexities. Additionally, the integration of Python scripting facilitates seamless models running, data processing, visualization, and sensitivity analysis, further enhancing the tool's utility for practitioners. This novel approach bridges the gap between the simplicity of 1D modelling and the complexity of higher-dimensional models, in particular in 3D. It offers a practical and efficient solution for draindown modelling in heap leach systems. This tool has the potential to streamline design and operational workflows by reducing reliance on resource-intensive modelling techniques
Habitat Selection and Resource Use by Bighorn Sheep Following Translocation
No full textRecovery of North American ungulates is one of the greatest conservation success stories of the 20th century. Following widespread extirpations resulting from novel diseases, habitat fragmentation, and overharvest leading up to the 20th century, numerous conservation measures were implemented by Federal and State governments to restore struggling populations of wild sheep, elk, deer, goats, and other wildlife. One of the most effective restoration tools to emerge from this era was translocation of individuals by physically moving them from healthy source populations to formerly occupied habitats. This process became an essential management tool, particularly for restoring isolated populations of ungulates. Yet, despite numerous successful translocations, the tool remains an imperfect solution. Translocated ungulates continue to face ecological and physiological challenges in adapting to new habitats. Asynchronous topographical, climatic, and environmental conditions between source and target habitats can hinder population establishment and expansion, leading to low survival, low recruitment, and even local extirpation. Understanding how translocated ungulates select resources and adapt to new habitats is critical for improving long-term outcomes of conservation. Few ungulates epitomize the challenges of reintroduction by translocation than bighorn sheep (Ovis canadensis). Bighorn sheep were once extirpated throughout much of their native range but have since been reintroduced to their historic habitat throughout the western United States, Mexico, and Canada. To date, over 22,000 individuals have been translocated in North America. While many of these translocations have been successful in reestablishing population, numerous translocations have failed or resulted in low recruitment, high mortality, and limited range expansion. These unsuccessful reintroductions have allowed conservation practitioners and scientists to improve methodology for translocations, which now includes consideration of habitat similarities, distance to domestic sheep, and genetic and physiological adaptations to local conditions.In our first chapter, we sought to determine the environmental and topographic conditions that make translocations successful for long-term establishment of populations of bighorn sheep. We quantified selection of resources in a unique population of translocated bighorn sheep in western North Dakota and its corresponding source population in north-central Montana. We used resource selection functions to test the hypothesis that these two populations would select habitat with high-quality forage during spring green up, when access to forage is crucial to recruitment and survival of young. Additionally, we tested the hypothesis that female bighorn sheep in both study areas engage in a similar trade-off between vegetation and terrain that is safer for raising young following parturition. In support of our hypothesis, both the source population in Montana and the translocated population in North Dakota selected habitat with access to nutrition pre-parturition and shifted to safer habitat post-parturition. Interestingly, some differences in selection emerged between the two populations, reflecting the distinct topographic and environmental characteristics of the two study areas, despite their geographic proximity.
In our second chapter, we examined the influence of ecotypic variation between two populations of bighorn sheep translocated from different regions to the same area. We hypothesized that ecotypic variation, defined as a set of characteristics that restrict a subgroup of animals to a narrow range of environmental conditions, would result in contrasting outcomes of selection of resources in the two translocated populations. To examine differences between translocated ecotypes, we quantified selection of resources by bighorn sheep translocated from Rocky Boy’s Reservation, Montana, and from Morenci, Arizona to the same release site in Antelope Island, Utah. The population in Antelope Island provided a unique opportunity to explore differences in selection of resources between two ecotypes of Rocky Mountain bighorn sheep. We used resource selection functions to test hypotheses that 1) selection of resources in Utah by bighorn sheep translocated from Montana would contrast with that of bighorn sheep translocated from Arizona, 2) bighorn sheep translocated from Montana would signal a phenological mismatch between the timing of parturition and availability of forage, and 3) bighorn sheep from Montana would select rugged, steep terrain more strongly than bighorn sheep translocated from Arizona.
In support of our first hypothesis, selection of resources by bighorn sheep translocated from Montana contrasted significantly with bighorn sheep translocated from Arizona. Our second hypothesis was supported by the limited selection of forage by Montana bighorn sheep, although there is reason to believe these bighorn sheep may adjust their selection of resources on Antelope Island. Finally, we found strong support for our hypothesis that bighorn sheep translocated from Montana consistently selected rugged, steep terrain more strongly compared to bighorn sheep translocated from Arizona. These findings further reinforce the necessity of considering ecotypic variation when translocating bighorn sheep
Consequence Classification Approach for Heap Leach Pad Design
No full textThis paper was presented at the Heap Leach Solutions Conference, October 19-21, 2025, Sparks, Nevada.Heap leach pads are structures used in mining for the extraction of metals such as gold, copper, nickel, and uranium. While heap leach pad failures have been very rare throughout history and have typically generated minor impacts compared to tailings dams, they can shut down mining production, significantly affecting the profitability of the operation. Therefore, it is crucial to implement design procedures that minimize the risks associated with these facilities. This paper proposes applying a similar approach used for tailings dam classification to heap leach pad projects, based on the consequences of a potential failure. This approach is based on the recommendations of international guidelines such as the Canadian Dam Association (CDA, 2019) and the Global Industry Standard on Tailings Management (ICMM, 2020), which establish categories of failure consequences for tailings dams ranging from low to extreme. This involves a detailed analysis of aspects such as the proximity of operating personnel and populations, the vulnerability of nearby ecosystems, the infrastructure that could be affected, and the economic, social, and cultural repercussions of a potential heap leach pad collapse. This classification will allow determining the return periods for seismic and flood events that must be considered in the design of these facilities, thus reducing the risk of failure. Due to the limited impacted areas, high return periods are not expected to be used, at least during the operational phase. Implementing this procedure involves quantifying the magnitude of potential impacts to support classification. Adopting this methodology not only seeks to improve the safety and efficiency of leach pads, but also ensures operational continuity of mining operations, avoiding costly interruptions and promoting more responsible and sustainable mining practice
Pheromones, nectar, and computer vision: investigating large-scale patterns of chemodiversity.
No full textAt its core, chemical ecology attempts to describe the complex forces that generate the vast chemical diversity observed in nature and which allow natural products to mediate interactions across biological scales. Chemodiversity is a major component of functional biodiversity in all ecosystems, and in plant – insect interactions especially mediates mutualisms, antagonisms, defense, reproduction, and many more fundamentally important ecosystem processes. Working with large datasets across multiple insect systems, this dissertation provides insight into major drivers of chemical diversity across taxa, ecological functions of specialized chemistry in plant – pollinator interactions, and methods of automating data collection for monitoring insect diversity. In particular, I investigate the role of specialized nectar chemistry under several existing hypotheses, generate and test novel hypotheses regarding global patterns of insect semiochemical diversity, and discuss methods for rapidly and scalably monitoring insect biodiversity using machine learning. Chapter 1 builds a framework for understanding large scale patterns of chemodiversity. Here I apply ideas from information theory to semiochemical communication, especially concepts surrounding communication in noisy channels, to predict variation in semiochemical diversity at global scales. While an understanding of chemical noise allows the interpretation of existing hypotheses in the chemical ecology of plant and animal systems, it also suggests several patterns which have yet to be observed. Testing two predictions made under this framework, that insect volatile semiochemical blends should be less rich in biodiverse regions and contact semiochemicals more rich, using a large scale meta-analysis approach, I show that insect semiochemistry broadly follows the patterns expected under information theory. These results suggest that large scale patterns of chemodiversity may arise due to selection imposed by chemical noise, and that information theory can allow interpretation of these patterns across systems.
Chapter 2 investigates a specific aspect of chemical communication: the role of specialized nectar chemistry in mediating plant – pollinator interactions. The Nectar Pleiotropy hypothesis suggests that the presence of specialized metabolites in nectar is non-adaptive, and that these compounds are maintained in the nectar solely through selective pressures for antiherbivore chemistry in leaves and floral tissues. Using common garden experiments with milkweed flowers in Idaho and Arizona, I found that nectar chemistry is consistently distinct from leaf chemistry within and across species, with large numbers of nectar compounds not found in leaves, and the concentrations of the majority of shared compounds uncorrelated between leaves and nectar. These findings demonstrate that the Nectar Pleiotropy hypothesis does not apply to most compounds in the milkweed nectar metabolome, suggesting that nectar chemistry is maintained separately from leaf chemistry to mediate interactions with nectar feeding organisms.
Chapter 3 builds on the work of chapter two by directly linking milkweed nectar chemistry to pollinator visitation, behavior, and plant fitness. Using gardens of milkweeds in Nevada and California, I show that compounds which strongly associate with insect feeding are also strongly associated with average pollinator efficiency, and that the concentration of these compounds in nectar indirectly alters plant fitness through the manipulation of high efficiency pollinators, but not nectar larcenists. These results indicate that nectar chemistry in these species acts primarily to manipulate high quality pollinating insects, with little impact on non-pollinating insects.
Finally, chapter 4 moves from interrogations of chemical diversity in plants and insects to discuss methods of monitoring insect biodiversity as a whole. Here I developed a computer vision system and data pipeline for rapidly gathering training data and deploying automated systems for insect monitoring. By incorporating hierarchical data into computer vision models, I demonstrate that model performance can be greatly improved when working in systems where the majority of taxa are unknown to science. These systems, while only capable of measuring a specific dimension of insect diversity, pave the way to more generalized and scalable models for automated insect monitoring
The Economics of Crime and Punishment: A Computational Approach
No full textEmergent characteristics of crime rates and law enforcement are observed empiricallyin cities. For example, spatial clustering of crimes is observed. It is difficult to explain
this clustering with Becker’s crime model using representative agents. We extend
Becker’s model using an agent-based approach to explain this phenomenon. First,
we develop a grid model of a city with agents located in housing. We allow those
agents to decide whether to burgle a house in their spatial location. A government
agent allocates resources to fines or law enforcement. We show how these agents’
interactions lead to endogenous criminals and clustering of high-crime areas. We then
demonstrate that this model also produces results found in empirical literature and
how this method could be used to evaluate policy in real-world cities with relaxed
assumptions about human decision-making
Nevada State Climate Office Quarterly Report March-May 2025
No full textQuarterly Report and Outlook for notable weather and climate in Nevada through March-May 2025. English Version