Inland empire wildlife bingo

Game designed to stimulate an interest in wildlife conservation and local history

Regional Similarity of Leveed Lava Flows on the Mars Plains

The dynamics of lava flow movement are controlled by the fluid interior. Crust, solids, and nondeformable material can only retard the advance or spreading of a lava flow. Figure 1 shows a typical large, channelized lava flow found on the Mars plains. It has been suggested in [I] that such large leveed flows on the Mars plains were emplaced by a balance between the formation and shedding of crust as the flow advances. For the prototypical flow north of Pavonis Mons (Fig. I), such a balance leads to a flow morphology that approximately self-replicates at all locations along the flow path [2,3]. Moreover, most quantitative characteristics of emplacement (e.g., viscosity, volumetric flow rate) of the prototype flow at Pavonis Mons resembled those of large channelized lava flows on Earth. The exception was the relatively long, sustained supply of lava, on the order of a year as opposed to hours or days for terrestrial analogs

Exploring Inflated Pahohoe Lava Flow Morphologies and the Effects of Cooling Using a New Simulation Approach

Pahoehoe lavas are recognized as an important landform on Earth, Mars and Io. Observations of such flows on Earth (e.g., Figure 1) indicate that the emplacement process is dominated by random effects. Existing models for lobate a`a lava flows that assume viscous fluid flow on an inclined plane are not appropriate for dealing with the numerous random factors present in pahoehoe emplacement. Thus, interpretation of emplacement conditions for pahoehoe lava flows on Mars requires fundamentally different models. A new model that implements a simulation approach has recently been developed that allows exploration of a variety of key influences on pahoehoe lobe emplacement (e.g., source shape, confinement, slope). One important factor that has an impact on the final topographic shape and morphology of a pahoehoe lobe is the volumetric flow rate of lava, where cooling of lava on the lobe surface influences the likelihood of subsequent breakouts

Inferred Lunar Boulder Distributions at Decimeter Scales

Block size distributions of impact deposits on the Moon are diagnostic of the impact process and environmental effects, such as target lithology and weathering. Block size distributions are also important factors in trafficability, habitability, and possibly the identification of indigenous resources. Lunar block sizes have been investigated for many years for many purposes [e.g., 1-3]. An unresolved issue is the extent to which lunar block size distributions can be extrapolated to scales smaller than limits of resolution of direct measurement. This would seem to be a straightforward statistical application, but it is complicated by two issues. First, the cumulative size frequency distribution of observable boulders rolls over due to resolution limitations at the small end. Second, statistical regression provides the best fit only around the centroid of the data [4]. Confidence and prediction limits splay away from the best fit at the endpoints resulting in inferences in the boulder density at the CPR scale that can differ by many orders of magnitude [4]. These issues were originally investigated by Cintala and McBride [2] using Surveyor data. The objective of this study was to determine whether the measured block size distributions from Lunar Reconnaissance Orbiter Camera - Narrow Angle Camera (LROC-NAC) images (m-scale resolution) can be used to infer the block size distribution at length scales comparable to Mini-RF Circular Polarization Ratio (CPR) scales, nominally taken as 10 cm. This would set the stage for assessing correlations of inferred block size distributions with CPR returns [6]

Comparing Volcanic Terrains on Venus and Earth: How Prevalent are Pyroclastic Deposits on Venus?

In the last several years, astronomers have discovered several exoplanets with masses less than 10 times that of the Earth [1]. Despite the likely abundance of Earth-sized planets, little is known about the pathways through which these planets evolve to become habitable or uninhabitable. Venus and Earth have similar planetary radii and solar orbital distance, and therefore offer a chance to study in detail the divergent evolution of two objects that now have radically different climates. Understanding the extent, duration, and types of volcanism present on Venus is an important step towards understanding how volatiles released from the interior of Venus have influenced the development of the atmosphere. Placing constraints on the extent of explosive volcanism on Venus can provide boundary conditions for timing, volumes, and altitudes for atmospheric injection of volatiles. In addition, atmospheric properties such as near-surface temperature and density affect how interior heat and volatiles are released. Radar image data for Venus can be used to determine the physical properties of volcanic deposits, and in particular, they can be used to search for evidence of pyroclastic deposits that may result from explosive outgassing of volatiles. For explosive volcanism to occur with the current high atmospheric pressure, magma volatile contents must be higher than is typical on Earth (at least 2-4% by weight) [2,3]. In, addition, pyroclastic flows should be more prevalent on Venus than convective plumes and material may not travel as far from the vent source as it would on Earth [3]. Areas of high radar backscatter with wispy margins that occur near concentric fractures on Sapho Patera [4] and several coronae in Eastern Eistla Regio [5] have been attributed to collapse of eruption columns and runout of rough materials

A New Approach to Inferences for Pancake Domes on Venus

Figure 1 shows a radar image and topography for flat-topped, steep-sided "pancake" domes on Venus. At least 145 such domes have been identified on Venus [I] and are thought to be volcanic in origin [2]. Based on analysis of the dome surfaces, [3] suggested that only the late stage surface fractures are preserved, indicating entrainment and annealing of fractures during emplacement, consistent with a basaltic composition. Figure 1 shows a radar image and topography for flat-topped, steep-sided "pancake" domes on Venus. At least 145 such domes have been identified on Venus [I] and are thought to be volcanic in origin [2]. Based on analysis of the dome surfaces, [3] suggested that only the late stage surface fractures are preserved, indicating entrainment and annealing of fractures during emplacement, consistent with a basaltic composition

Emplacement Scenarios for Volcanic Domes on Venus

One key to understanding the history of resurfacing on Venus is better constraints on the emplacement timescales for the range of volcanic features visible on the surface. A figure shows a Magellan radar image and topography for a putative lava dome on Venus. 175 such domes have been identified with diameters ranging from 19 - 94 km, and estimated thicknesses as great as 4 km. These domes are thought to be volcanic in origin and to have formed by the flow of viscous fluid (i.e., lava) on the surface

Normative Evidence Accumulation in Unpredictable Environments

In our dynamic world, decisions about noisy stimuli can require temporal accumulation of evidence to identify steady signals, differentiation to detect unpredictable changes in those signals, or both. Normative models can account for learning in these environments but have not yet been applied to faster decision processes. We present a novel, normative formulation of adaptive learning models that forms decisions by acting as a leaky accumulator with non-absorbing bounds. These dynamics, derived for both discrete and continuous cases, depend on the expected rate of change of the statistics of the evidence and balance signal identification and change detection. We found that, for two different tasks, human subjects learned these expectations, albeit imperfectly, then used them to make decisions in accordance with the normative model. The results represent a unified, empirically supported account of decision-making in unpredictable environments that provides new insights into the expectation-driven dynamics of the underlying neural signals