VictorJuiz/thesis huggingface dataset:
We Are What We Eat: The Impact of Agricultural Intensity on the Microbiome of Honeybee Guts

Currently, Apis mellifera, commonly known as honeybees, are dying at alarming rates. Beekeepers across the United States lost 45.5% of their managed honeybee colonies from April 2020 to April 2021. This turnover rate is staggeringly high and has been growing. This loss poses a food security and economic problem. Recent studies have shown that diet and the microbes living in honeybee guts are a critical part of this picture. These microbes influence weight gain, energy metabolism, nutrient uptake, development, and immune function. Research shows that higher agricultural intensity decreases the diversity of microbial communities in the nectar of crops. In this thesis, we connect these findings by exploring how agricultural intensification impacts the diversity of the honeybee gut microbiome. To perform this research, live honeybees were collected from both organic and conventional almond farms. The honeybee’s crop stomach and intestine were isolated and the bacterial DNA present was sequenced. These results show that conventional farming practices significantly lower the diversity of the microbiome of honeybees' guts, particularly in the intestinal tract. Given the critical role of intestinal microbes in honeybee health,  the reduced microbial diversity caused by agricultural disturbance could be a contributing factor to honeybee decline. With this information, farmers can make better informed choices about their practices and researchers can continue to pursue microbial solutions to honeybee decline.

Keywords:  Department of Biology
Apis mellifera
agricultural intensity
Conventional farming
Organic farming
Plant-pollinator relationships
Alpha diversity
Beta diversity
Gut microbiota
Microbial diversity
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Thesis
Advisor: Fukami Tadashi
Peay Kabir
We Miss 100% of the Shots We Don't Take: Identifying Opportunities in Mental Health Services for Collegiate Student-Athletes

The deliverables being uploaded have been created for the Stanford Policy, Organization, Leadership, Studies culminating Field Project. The masters recipient, Kayla Constandse, undertook this research to identify opportunities to address collegiate student-athlete mental health crises, particularly exacerbated by the COVID-19 pandemic. The findings demonstrate the necessity for athletic departments to further invest in their coaches.

Keywords: Student-Athlete
Mental Health
Higher Education
Text
Thesis
Weaponized whiteness : the causes and consequences of white identity politics at the extremes

This dissertation delves into the profound role of whiteness in the United States, with a specific focus on the impact of racial identity "at the extremes" in the American sociopolitical landscape. Across all three papers, I posit that whiteness transcends being a benign facet of one's self; rather, it can represent a driving force behind political ideology, perception, and behavior. This research extends the existing political science literature, which highlights the influential role of group membership and emotional connections in shaping individual political attitudes and actions. The first paper breaks new ground by focusing on the extreme and exceptional expressions of white identity, particularly white nationalism, delving into its definition, causes, and consequences. This research forms a foundational understanding of how extreme manifestations of white identity, as epitomized by white nationalism, can be leveraged in the political sphere. Furthermore, this paper identifies a robust connection between white nationalism and concerns about white erasure, which are rooted in fears of losing social status and hierarchy. This notion of "racial status threat" plays a pivotal role in comprehending the politics of white Americans, driving the exploration of how concerns about demographic replacement affect white American politics in the second and third papers. The second paper delves into the trajectory of the "great replacement" theory and its influence on contemporary political discourse. Through a multi-disciplinary approach, incorporating computer science and large language modeling, I explore the mainstreaming of the "great replacement," tracking the adoption of replacement rhetoric by influential media outlets and political figures. Notably, the research reveals that elites play a substantial role in amplifying this divisive messaging. The third paper extends this work by illustrating how this elite mainstreaming genuinely influences the politics of white Americans. It employs a pre-registered survey experiment involving white American participants and exposes that elite discourse on the "great replacement" significantly influences white Republican participants, leading to changes in their perceptions of whites being "erased," support for the politics of white solidarity, and fears of a changing American demographic landscape. Collectively, these three papers aim to illuminate the intricate relationship between extreme racial identity and its profound consequences on contemporary American politics. The dissertation fills critical gaps in existing literature, offering valuable insights into the political weaponization of racial identity and narratives. In highlighting how extreme theories rooted in demographic fears can infiltrate and shape political discourse, scholars can develop new understandings of white identity politics -- particularly at the extremes.

Keywords: Humanities and Sciences.
Political Science.
Advisor: Davenport Lauren
Jefferson Hakeem
Willer Robert Bartley
Well Production Forecasting Using Modern Deep Learning Models

Accurate well rate forecasting is essential for successful field development in the oil industry. Recurrent-based deep learning models have been used for production forecasting. However, recent advancements in the field have led to the use of transformer and transfer learning to overcome the need for large amounts of data. This dissertation presents an approach to using modern deep learning algorithms for oil production forecasting.
To enhance the accuracy of oil rate predictions in the Norwegian Volve and Norne fields, a combination of statistical models and deep learning models were investigated. These models included Autoregressive Integrated Moving Average (ARIMA), Light Gradient Boosting Machine (LightGBM), Block Recurrent Neural Network (BlockRNN), Temporal Fusion Transformer (TFT), and the Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS) using transfer learning. The models used multivariate real time historical data, such as bottomhole pressure, wellhead pressure, wellhead temperature, and choke size, as input features to predict the oil rate of two wells in each field. The models were trained on 85% of the data and tested on the remaining 15%, with the advanced models TFT and N-BEATS being compared to the conventional models in terms of prediction performance.
The complex production data used in this forecasting problem showed no clear trends or seasonality. The advanced deep learning models, the Temporal Fusion Transformer (TFT) and the Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS), outperformed other models in terms of forecasting accuracy. The TFT model was able to significantly minimize the testing Mean Squared Error (MSE). Additionally, the model predicted a range of uncertainty between the 10th and 90th percentiles to consider the variability in the blind test intervals. The N-BEATS transfer learning model was better at capturing dynamic time series features from the M4 dataset and applying that knowledge to predict oil rates in the Norne field, without any input variables like reservoir pressure. The N-BEATS approach was superior to all other models in terms of the difference between the forecast and actual rate, resulting in a mean square error of 0.02 for well F-12 and 0.05 for well F-11 respectively.
Previously, machine learning and deep learning techniques in the petroleum sector mainly utilized only historical field data for their predictions. However, our study highlights the potential of transfer learning and the N-BEATS model in green fields or newly developed areas where historical data are scarce. Additionally, the TFT probabilistic deep learning model showed outstanding results, outperforming traditional models, and providing a range of forecast uncertainty, which is very useful in making well-informed decisions in field development.

Keywords: Forecasting
Oil Rate
Deep learning (Machine learning)
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Thesis
Advisor: Horne Roland
"We're part of something way bigger" : the transformative potential of black teacher fugitive space

Growing concerns about antiblackness in education and the related attrition of Black teachers highlight the need for research on professional development spaces that support Black teacher retention. Drawing on ross's (2021) notion of educational fugitive space, this dissertation research is a longitudinal examination of how Black teachers co-create a Black teacher fugitive space, and how this space informs and supports their pedagogies and navigation of antiblackness at their school sites. Fugitivity is what Ford (2014) called the "artful escape of objectification" (p. 4), and it connotes an enslaved person who runs away from anti-Black horrors toward a freedom dream. This dissertation project employs these notions of educational fugitivity from Black Studies to theorize how these Black-affirming places are rehumanizing and sustaining for Black teachers, offering implications for Black teacher retention. I used participant observation methods to follow 20 Black teachers for a year in the Black Teacher Project (BTP), a professional development racial affinity space that I theorize is a fugitive space. In a subsequent year, I followed a subset of Black teachers from the BTP into their classrooms to study how this fugitive space impacts their pedagogies and their students' response to them. This project thus extends research on Black teacher retention by focusing on how Black-affirming fugitive spaces can support teachers in collectively reimagining schools as sites of liberated learning for their students.

Keywords: Education.
Advisor: Martínez Ramón
Ross Kihana
Rosa Jonathan
What it is to see : artificial vision as constitutive intra-action

Visual prosthesis (VP) devices—devices that electrically stimulate the visual system with the goal of restoring vision to individuals who have lost it—have been heralded as being at the forefront of a scientific frontier that will allow for seamless integration between the biological and the mechanical. However, while hundreds of individuals have been implanted with VPs, few data are available on the perceptual experience associated with the devices. What does an individual see when the device is turned on for the first time, how does that change over time, and what does it mean for one of these devices to "work"? This dissertation is based on four years of interdisciplinary ethnographic research in which I tracked retinal implant devices from bench to bedside. What I found was that the reason we don't know what artificial vision "is like" is because we don't ask, and why we don't ask is the story of clinical research and practice in the age of translational science and medicine, as well as the story of perception in the age of information and financialization. This has resulted in a biomedical landscape in which basic questions about the function of the devices have become practically unaskable, and what it means for the devices to "work" virtually irrelevant. When we do ask, however, we discover that there are large discrepancies between what was expected of these devices and what has been produced in terms of the perceptual experience of recipients, and that the "artificial vision" enabled by these devices is reported to be fundamentally different than what the individual remembered natural vision to be like. I argue that the failure of the devices stems in part from the problematic theory of vision that informed their design and implementation. I introduce an alternative theory of perception based on the work of empirical psychologist James J. Gibson that sees perception as the "direct" product of a constitutive intra-action of a perceiver-environment system (Erickson-Davis & Corwin, 2020). I show how a theory of perception as constitutive interaction (PCI) is more congruous with recipient reports and enables us to see that "artificial vision, " or the perceptual experience associated with the VP, is a unique perceptual phenomenon. It is a finding that enables us to critically reflect on not only this class of devices, but also the dominant theories of perception that informed their design and implementation

Keywords: Humanities and Sciences.
Anthropology.
Advisor: Jain Sarah S. Lochlann
Dumit Joseph
Luhrmann T. M. (Tanya M.)
Martinez Nicole
What matters? : how beginning and mid-career secondary science teachers perceive and negotiate instructional influences

Teachers' experiences with a variety of learning settings over the course of their careers raises important questions about how teachers navigate different messages and, ultimately, what ideas about science instruction they enact in the classroom. With the goal of better supporting science teachers, this dissertation seeks to broaden, and perhaps complicate, analytical lenses of teacher learning by considering the multitude of teachers' learning experiences. Specifically, the three papers in this dissertation each explore the question, "How do secondary science teachers' perceive and negotiate various instructional influences?" The first contribution of this dissertation is to document the various perceived influences. Not only are teacher education and teachers' school contexts considered influential, but so too, are more personal aspects, such as family and culture. Second, the dissertation complicates ideas of teacher learning across place and emphasizes the importance of supporting teachers to implement practices in their specific teaching contexts. Finally, the work proposes a weighting framework for how teachers differentially negotiate instructional influences. Implications for pre- and in-service teacher education are discussed.

Keywords: Education.
Advisor: Borko Hilda
Brown Bryan
Lee Victor
McFarland Daniel
Pope Denise
When and where we enter : black girl, English teachers, and the teaching of enslavement

When teachers engage in lessons about the institution and legacy of enslavement in the United States, they often do so in ways that inflict harm on Black students in general and Black girls in particular. This harm comes from the repetition of inaccurate information and/or a reluctance to acknowledge the lasting legacy. Such harm is known as curriculum violence and can create an impediment for learning for Black students while also reifying false information for others. This study examines how teachers engaging in teaching texts about enslavement were able to center Black girls in their instruction. In order to gain this understanding, I recruited teachers for participation in a community of practice focused on the experiences of Black girls in school. In addition, I worked with the teachers as they evaluated curriculum for instruction. Lastly, I observed their classrooms while they taught their curriculum. Throughout these phases, I utilized qualitative research methodologies of participant observation, interviews, and document analysis. Findings from this research reveals the challenges teachers face in prioritizing Black girls when addressing themes of enslavement and its legacy in the United States affirmed Black girls required them having Black girls in their classrooms. This study also revealed that while Black girls may or may not be present in the room, the work of prioritizing their experiences, particularly when teaching about enslavement must be addressed. Recommendations based on this work speak to the need for credentialling programs to provide more robust preparation for teachers so that they are equipped to navigate conversations about racism and oppression and avoid inflicting curriculum violence.

Keywords: Education.
Advisor: Garcia Antero
Ball Arnetha
Martinez Ramon
Where, Who, and How? Understanding Spatial Effects on Auction Outcomes and Bidder Strategy in the Rural Digital Opportunity Fund Phase I Auction (Auction 904)

FCC Auction 904 (Rural Digital Opportunity Fund Phase I) allocated 9.2 billion dollars out of an initially allocated 16 billion dollars for high-speed rural internet access subsidies. The auction was structured as a simultaneous descending clock auction across 61,766 census block groups (CBGs) and 286 bidders. In this paper, we propose a geospatial model of bidder value. We specifically concentrate on assessing competitiveness relative to the bidders’ existing service footprints. Geospatial organization strongly impacts auction outcomes, with neighboring/existing bidders going ~0.2 rounds further in post clearing round bids. Further, existing bidders who do not win are extremely strong competitors pushing winners by ~0.7-0.8 rounds more than wild tract second-place bidders. Finally, these results are robust to bidder-fixed effects and the inclusion of satellite bidders.

Keywords: Auctions
United States. Federal Communications Commission
Internet
Rural development
Subsidies
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Thesis
Advisor: Rosston Gregory
Lam Sarah
Wallsten Scott
Who feels they contribute to U.S. society? Helping behaviors and social class disparities in perceived contributions

Americans in lower social class contexts (SCC) are less likely to believe they contribute to society than Americans in higher SCC. Seven studies (N = 7,781) investigate whether one source of the social class disparity in perceived contributions is a cultural model that considers helping distant others (i.e., bridging help, e.g., volunteering) as more of a contribution to society than helping close others (i.e., bonding help, e.g., caring for family members). Chapter 1 introduces the U.S. cultural model of social good and posits that the differential value placed on bridging versus bonding help relates to social class inequality. Certain forms of bridging help tend to be relatively more practiced in higher (vs. lower) SCC, whereas certain forms of bonding help tend to be relatively more practiced in lower (vs. higher) SCC. Thus, valuing bridging help over bonding help can undermine the contributions of Americans in lower SCC. Chapter 2 presents evidence that Americans in lower (vs. higher) SCC perceive they contribute less to society. In Chapter 3, three experiments and a correlational study provide evidence for the U.S. cultural model of social good that may be a potential source of these disparities; Americans across SCC, and even helpers themselves, perceive bridging help as more of a contribution to society than bonding help. Further, Chapter 3 identifies the perceived choice to help as one reason why bridging help is perceived as more of a contribution than bonding help. Chapter 4 investigates how bridging and bonding help relate to social class disparities in perceived contributions to society and explores possible implications for well-being. Together, these studies suggest that people in lower SCC can experience a psychological inequality not because they do less for others but because what they do more of counts less as a contribution. This dissertation raises novel questions about sociocultural variation in prosociality and the role of doing good for others in individual and collective well-being.

Keywords: Humanities and Sciences.
Psychology.
Advisor: Eberhardt Jennifer
Markus Hazel
Walton Greg
Widespread capacity for conformational memory in the human proteome

Biomolecular condensation is a fundamental principle of cellular organization. In extreme cases, this collective behavior can store and transmit information, driving cellular memory with prion-like qualities. Although multiple examples have been identified in microbes, this behavior is assumed to be rare in metazoans. By coupling changes in protein stability to cellular fitness, we generated a quantitative atlas of self-assembly across the human proteome. Spanning multiple orders of magnitude, these measurements reveal that at least one-quarter of human proteins have the capacity to self-assemble; nearly three-quarters of these can persist over many cell divisions. This conformational memory was strongly enriched among proteins involved in key developmental decisions, stress responses, and aging. When purified in vitro these proteins formed assemblies that could autonomously replicate. Yet most did not form amyloid and were not overtly toxic. Moreover, the protein domains necessary for these behaviors often did not resemble those in classical prions. Examining a large library of human genetic variants, we find that disease-associated mutations commonly perturb conformational memory. Our results suggest that the capacity to store and transmit information is ubiquitous in the human proteome and that its dysfunction is a central feature of aging and disease

Keywords: Engineering.
Bioengineering.
Advisor: Jarosz Daniel
Fischbach Michael
Wysocka Joanna