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Land inequality stalls economic development, entrenches poverty, and is associated with environmental degradation. Yet, rigorous assessments of land-use interventions attend to inequality only rarely. A land inequality lens is especially important to understand how recent large-scale land acquisitions (LSLAs) affect smallholder and indigenous communities across as much as 100 million hectares around the world. This paper studies inequalities in land assets, specifically landholdings and farm size, to derive insights into the distributional outcomes of LSLAs. Using a household survey covering four pairs of land acquisition and control sites in Tanzania, we use a quasi-experimental design to characterize changes in land inequality and subsequent impacts on well-being. We find convincing evidence that LSLAs in Tanzania lead to both reduced landholdings and greater farmland inequality among smallholders. Households in proximity to LSLAs are associated with 21.1% (P = 0.02) smaller landholdings while evidence, although insignificant, is suggestive that farm sizes are also declining. Aggregate estimates, however, hide that households in the bottom quartiles of farm size suffer the brunt of landlessness and land loss induced by LSLAs that combine to generate greater farmland inequality. Additional analyses find that land inequality is not offset by improvements in other livelihood dimensions, rather farm size decreases among households near LSLAs are associated with no income improvements, lower wealth, increased poverty, and higher food insecurity. The results demonstrate that without explicit consideration of distributional outcomes, land-use policies can systematically reinforce existing inequalities. [ABSTRACT FROM AUTHOR]

Generative AI ("GenAI") systems such as ChatGPT recently have developed to the point where they can produce computer-generated text and images that are difficult to differentiate from human-generated text and images. Similarly, evidentiary materials such as documents, videos, and audio recordings that are AI-generated are becoming increasingly difficult to differentiate from those that are not AI-generated. These technological advancements present significant challenges to parties, their counsel, and the courts in determining whether evidence is authentic or fake. Moreover, the explosive proliferation and use of GenAI applications raises concerns about whether litigation costs will dramatically increase as parties are forced to hire forensic experts to address AI-generated evidence, the ability of juries to discern authentic from fake evidence, and whether GenAI will overwhelm the courts with AI-generated lawsuits, whether vexatious or otherwise. GenAI systems have the potential to challenge existing substantive intellectual property ("IP") law by producing content that is machine, not human, generated, but that also relies on human-generated content in potentially infringing ways. Finally, GenAI threatens to alter the way in which lawyers litigate and judges decide cases. This article discusses these issues, and offers a comprehensive, yet understandable, explanation of what GenAI is and how it functions. It explores evidentiary issues that must be addressed by the bench and bar to determine whether actual or asserted (i.e., deepfake) GenAI output should be admitted as evidence in civil and criminal trials. Importantly, it offers practical, step-by-step recommendations for courts and attorneys to follow in meeting the evidentiary challenges posed by GenAI. Finally, it highlights additional impacts that GenAI evidence may have on the development of substantive IP law, and its potential impact on what the future may hold for litigating cases in a GenAI world. [ABSTRACT FROM AUTHOR]

To learn how household characteristics might affect carbon storage on exurban residential parcels, we conducted a web survey of 126 southeast Michigan exurban homeowners. We measured several behaviors that may affect carbon storage: the proportion of parcel mown (or left unmown with woody vegetation), the number of large trees retained, the number of trees planted, how leaf litter was managed, the use of fertilizer, and the use of irrigation. We investigated whether these behaviors might be related to parcel size, homeowners’ concern for having a yard that fits neighborhood norms, or household demographic characteristics. We found that owners of large (>0.53 ha) parcels were consistently different from small and medium parcel owners in their management behaviors. Large parcel owners mowed a smaller proportion of their parcels. They also were less likely to fertilize and irrigate, had planted more trees, and had more large trees on their parcels. In addition, they reported being less affected by a desire for their yard to fit neighborhood norms. Our results suggest that parcel size and neighborhood norms together affect landscape behaviors that affect carbon storage. We conclude that for large parcels, size alone may promote carbon-storing management behaviors. However, for smaller parcels, governance should promote appropriate design at the scale of whole blocks or subdivisions in order to drive adoption and acceptance across neighborhoods. [ABSTRACT FROM AUTHOR]

Grassland degradation is serious in the Mongolian plateau, especially in Inner Mongolia, China. Accurate monitoring of grassland types and qualities is increasingly important for the purposes of grassland conservation and restoration. Using in situ hyperspectral reflectance data and ground-based ecological measurements, we explored the potential for large-scale monitoring grassland communities using imaging spectroradiometers. We compared the spectral reflectance of the major types of grasslands and field plots with/without livestock grazing. We also did statistical analysis about the relationship between hyperspectral indices and aboveground biomass (AGB) of the surveyed grassland communities. The results showed that: (1) the dominant plant species varied across meadow, typical, and desert steppe, and they also varied between fenced and grazed plots; (2) in situ hyperspectral data are useful for differentiating grassland communities of meadow, typical, and desert steppe and grassland communities with and without livestock grazing; and (3) the prediction accuracies of vegetation indices for AGB decreased from desert to typical and meadow steppe, and the results were contrary for the prediction accuracies of red edge inflection point (REIP). REIP may not be suitable for estimating AGB of the low-density grassland communities. The above results implied that care must be taken while using statistical models to link spectral and ecological measurements in large geographical scales since there is lack of portability over different types of grassland communities. This study provides foundations for future large-scale efforts of monitoring grassland communities in Inner Mongolia using imaging spectroradiometers. [ABSTRACT FROM AUTHOR]

We investigated whether exurban parcel size might be related to homeowners' behaviors that could affect carbon balance in residential landscapes. Based on data drawn from in-depth interviews and field surveys in 26 southeast Michigan exurban households, we conclude that an approximately 1-acre (0.45 ha) size may be a critical threshold for certain behaviors, such as leaving a portion of an exurban parcel in forest or old field or retaining leaf litter on the property. Tree planting is a relevant behavior that may increase when parcels are larger than 0.5 acres (0.20 ha). Yard style of adjacent properties also appears to be related to homeowners' landscape management behaviors: conventional turf-dominated yards prevail adjacent to parcels smaller than one-acre. We used our analysis of these data to develop an exurban homeowner typology to be employed in agent based models in which homeowners' behaviors that may affect carbon balance are linked to parcel size. [ABSTRACT FROM AUTHOR]

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To evaluate the impacts of urbanization on soil and vegetation in protected forest areas, 12 forest sites in Southeastern Michigan USA were studied in an indirect urban-rural gradient. Field study plots were established in forest edge zones of each protected area. Significant findings were that in these edge zones of protected areas: (a) soil nitrogen tended to be greater where surrounding housing density was greater; (b) overstory woody biomass and basal area were greater where surrounding housing density was greater; and (c) the study region overall exhibited low soil carbon content (mean 2.71%) and relatively high soil nitrogen content (mean 0.20%), yielding a surprisingly low surface soil C/N ratio (mean 13.4). Overall, 24 woody plant genera were encountered, with the three genera Acer, Carya and Quercus accounting for 83.7% of total biomass and 74.1% of total basal area. No significant relationships were observed between housing density and soil C/N ratio or between housing density and foliar N. Results indicate that a halo of urban-ecological impacts exists in the landscape of Southeastern Michigan, similar to previously studied linear urban-rural gradients in other regions. [ABSTRACT FROM AUTHOR]

Abstract: Preterm birth is a public health issue of global significance, which may result in mortality during the perinatal period or may lead to major health and financial consequences due to lifelong impacts. Even though several risk factors for preterm birth have been identified, prevention efforts have failed to halt the increasing rates of preterm birth. Epidemiological studies have identified air pollution as an emerging potential risk factor for preterm birth. However, many studies were limited by study design and inadequate exposure assessment. Due to the ubiquitous nature of ambient air pollution and the potential public health significance of any role in causing preterm birth, a novel focus investigating possible causal mechanisms influenced by air pollution is therefore a global health priority. We hypothesize that air pollution may act together with other biological factors to induce systemic inflammation and influence the duration of pregnancy. Evaluation and testing of this hypothesis is currently being conducted in a prospective cohort study in Mexico City and will provide an understanding of the pathways that mediate the effects of air pollution on preterm birth. The important public health implication is that crucial steps in this mechanistic pathway can potentially be acted on early in pregnancy to reduce the risk of preterm birth. [Copyright &y& Elsevier]

Abstract: The increasing availability of satellite images derived from multiple sensors creates opportunities for broader spatial and temporal coverage but also methodological challenges. We present a geostatistical inverse modeling (GIM) approach for merging coarse-resolution images with variable resolutions and for super-resolution (i.e., predictions at the sub-pixel level) mapping of continuous spatial processes. GIM can explicitly account for the differences in spatial supports of multiple datasets. The restricted maximum likelihood method was used for parameter estimations associated with the change-of-support problem. We used GIM to produce both spatial predictions of a target image and prediction uncertainties, while preserving the values of original measurements. GIM is totally data driven, and covariance parameters for a target resolution can be directly derived from measurements. We also developed a moving-window GIM approach to accommodate spatial nonstationarity and reduce computational burden associated with large image data. First, we demonstrated GIM and moving-window GIM on synthetic images. Aggregated synthetic images with variable resolutions were merged to produce a single resolution image. The results show that the two approaches can produce accurate spatial predictions and generate prediction uncertainties. Second, we applied moving-window GIM for merging aerosol optical depth (AOD) data with variable resolutions, which were derived from two satellite sensors. The modeling results show that moving-window GIM can be applied for merging complementary AOD data from two sensors and for super-resolution mapping of global AOD distributions. Therefore, we can conclude that GIM is a practical solution for merging complementary coarse-resolution images and for super-resolution mapping of continuous spatial processes. [Copyright &y& Elsevier]

Highlights: [•] Livelihood adaptation strategies are different in Mongolia and Inner Mongolia. [•] Local institutions played key roles in shaping livelihood adaptation strategies. [•] Decreased grazing mobility has increased livelihood vulnerability of herders. [ABSTRACT FROM AUTHOR]

Highlights: [•] Agent-based modeling is a promising quantitative method for environmental research. [•] Cooperative use of pastures was important for climate change adaptation. [•] Agent diversity and social norms were effective for facilitating cooperation. [•] Kin selection and punishing free-riders were useful for maintaining cooperation. [ABSTRACT FROM AUTHOR]

Highlights: [•] We review approaches to modeling land change in scientific and practical applications. [•] Modeling approaches need to be considered relative to the different purposes they serve. [•] The profession needs to better integrate observational data, models across scales, and positive and normative approaches. [•] Key approaches to evaluating land-change models are identified. [Copyright &y& Elsevier]

Abstract: We present a quartet-based phylogeny algorithm that returns the correct topology for taxa in time with high probability, assuming that each quartet is inconsistent with the true tree topology with constant probability, independent of other quartets. Our incremental algorithm relies upon a search tree structure for the phylogeny that is balanced, with high probability, no matter the true topology. In experiments, our prototype was as fast as the fastest heuristics, but because real data do not typically satisfy our probabilistic assumptions, its overall performance is not as good as our theoretical results predict. [Copyright &y& Elsevier]

The article focuses on mixed methods in land change research. As stated, land change science has been strongly influenced by geographers working in geographic information science. It states that geographic information approaches are uniquely positioned to facilitate integration as several applications exist to integrate qualitative and quantitative data in geographic information system (GIS) frameworks.

Recently, we have identified a randomized quartet phylogeny algorithm that has O(n log n) runtime with high probability, which is asymptotically optimal. Our algorithm has high probability of returning the correct phylogeny when quartet errors are independent and occur with known probability, and when the algorithm uses a guide tree on O(log log n) taxa that is correct with high probability. In practice, none of these assumptions is correct: quartet errors are positively correlated and occur with unknown probability, and the guide tree is often error prone. Here, we bring our work out of the purely theoretical setting. We present a variety of extensions which, while only slowing the algorithm down by a constant factor, make its performance nearly comparable to that of Neighbour Joining , which requires Θ(n3) runtime in existing implementations. Our results suggest a new direction for quartet-based phylogenetic reconstruction that may yield striking speed improvements at minimal accuracy cost. An early prototype implementation of our software is available at http://www.cs.uwaterloo.ca/∼jmtruszk/qtree.tar.gz. [ABSTRACT FROM AUTHOR]

Abstract: Agent-based modelling has become an important tool to investigate socio-ecological processes. Its use is partially driven by increasing demand from decision makers to provide support for understanding the potential implications of decisions in complex situations. While one of the advantages of agent-based modelling is the ability to simulate the implications of human decision-making processes explicitly, methods for providing empirical support for the representation of the behaviour of human agents have not been structured systematically. This paper develops a framework for the parameterisation of human behaviour in agent-based models and develops twelve distinct sequences for the characterisation and parameterisation of human behaviours. Examples are provided to illustrate the most important sequences. This framework is a first step towards a guide for parameterisation of human behaviour in ABM. A structured discussion within the agent-based community is needed to achieve a more definitive guideline. [Copyright &y& Elsevier]

Imperfect and internal rhymes are two important features in rap music previously ignored in the music information retrieval literature. We developed a method of scoring potential rhymes using a probabilistic model based on phoneme frequencies in rap lyrics. We used this scoring scheme to automatically identify internal and line-final rhymes in song lyrics and demonstrated the performance of this method compared to rules-based models. We then calculated higher-level rhyme features and used them to compare rhyming styles in song lyrics from different genres, and for different rap artists. We found that these detected features corresponded to realworld descriptions of rhyming style and were strongly characteristic of different rappers, resulting in potential applications to style-based comparison, music recommendation, and authorship identification. [ABSTRACT FROM AUTHOR]

Global climate change is increasing the frequency of heat waves, hot weather, and temperature variability, which contribute to mortality and illness. Baseline information on local efforts to reduce heat vulnerability, including public advisories; minimizing greenhouse gas emissions; and mitigating urban heat islands, is lacking. We designed a survey about local government programs to prevent health problems and reduce heat exposure during heatwaves and administered it to 285 US communities. Of 70 respondents, 26 indicated that excessive heat events are a significant issue for the local government; 30 had established preventive programs. Local government leadership and public health impacts of heat were cited most frequently as extremely important determinants of preventive programs, followed by implementation costs, economic impacts of hot weather, and greenhouse gas emissions mitigation. Cool paving materials and vegetated roofs were common heat mitigation strategies. Fact sheets and case studies were desired guidance for protecting communities during hot weather. New partnerships and financial resources are needed to support more widespread local action to prevent adverse health consequences of climate change and promote environmental sustainability. [ABSTRACT FROM AUTHOR]

Urban areas are home to more than half of the world's people, responsible for >70% of anthropogenic release of carbon dioxide and 76% of wood used for industrial purposes. By 2050 the proportion of the urban population is expected to increase to 70% worldwide. Despite fast rates of change and potential value for mitigation of carbon dioxide emissions, the organic carbon storage in human settlements has not been well quantified. Here, we show that human settlements can store as much carbon per unit area (23–42 kg C m−2 urban areas and 7–16 kg C m−2exurban areas) as tropical forests, which have the highest carbon density of natural ecosystems (4–25 kg C m−2). By the year 2000 carbon storage attributed to human settlements of the conterminous United States was 18 Pg of carbon or 10% of its total land carbon storage. Sixty-four percent of this carbon was attributed to soil, 20% to vegetation, 11% to landfills, and 5% to buildings. To offset rising urban emissions of carbon, regional and national governments should consider how to protect or even to increase carbon storage of human-dominated landscapes. Rigorous studies addressing carbon budgets of human settlements and vulnerability of their carbon storage are needed. [ABSTRACT FROM AUTHOR]

We investigated the influence of remote sensing spatial resolution on estimates of characteristic land-cover change (LCC) and LCC-related aboveground biomass change (Δbiomass) in three study sites representative of the East Siberian boreal forest. Data included LCC estimated using an existing Landsatderived land-cover dataset for 1990 and 2000, and above-ground standing biomass stocks simulated by the FAREAST forest succession model and applied on a pixel basis. At the base 60 m resolution, several landscape pattern metrics were derived to describe the characteristic LCC types. LCC data were progressively degraded to 240, 480, and 960 m. LCC proportions and Dbiomasswere derived at each of the coarser resolutions and scale dependences of LCC and Δbiomass were analyzed. Compared to the base 60 m resolution, the Logged LCC type was highly scale dependent and was consistently underestimated at coarser resolutions. The Burned type was under- or over-estimated depending strongly on its patch size. Estimated at the base 60 m resolution,modeled biomass increased in two sites (i.e., 3.0 and 6.4 Mg C ha-1 for the Tomsk and Krasnoyarsk sites, respectively) and declined slightly in one site (i.e., -0.5 Mg C ha-1 for the Irkutsk site) between the two dates. At the degraded resolutions, the estimated Δbiomass increased to 3.3 and 7.0 Mg C ha-1 for the Tomsk and Krasnoyarsk sites, while it declined to -0.8 Mg C ha-1 for the Irkutsk site. Results indicate that LCC and Δbiomass values may be progressively amplified in either direction as resolution is degraded, depending on the mean patch size (MPS) of disturbances, and that the error of LCC and Δbiomass estimates also increases at coarser resolutions. [ABSTRACT FROM AUTHOR]