Geoscience Process and Racetrack Case Study Notes

Overview of how science is depicted and practiced

• Science is often messier than a linear, step-by-step sequence; researchers enter from many starting points and move back and forth between stages.
• A more representative depiction: input (questions from exploration and discovery, curiosity-driven pure science, or practical/community problems) → data gathering → hypotheses → testing and iteration → collaboration and feedback → publication and broader impact.
• Input sources for scientific questions:
  • Curiosity and desire to understand observed phenomena (pure science).
  • Practical community needs or problems (e.g., groundwater contamination, policy questions about mercury emissions).
  • Policy-driven questions that require scientific input to guide limits and regulations.
• Core activity: gathering data of various types (numerical, qualitative, visual) to test hypotheses and explain observations.
• The process is iterative and non-linear: scientists continually reformulate, observe more, and test new ideas.
• Science is collaborative, not done in isolation:
  • Collaboration across specialties brings diverse knowledge to a problem.
  • Results are published or presented; others in the scientific community critique and provide feedback (often rigorous, sometimes harsh) to improve explanations.
  • Interaction with policymakers and other stakeholders helps push science toward real-world applications and further questions.
• Outcomes and bidirectional influence:
  • Scientific findings inform policy, technology development, and practical solutions.
  • New technology leads to new observations and new questions, driving science forward.
• The nature of science is unfinished and evolving:
  • We reach policy guidance or recommendations, but future findings can revise these recommendations.
  • Embracing ongoing refinement is a core aspect of scientific practice.
• Question to consider: What are the implications of science being iterative, collaborative, and policy-relevant? Ethical and practical implications include transparency, reproducibility, avoiding bias, and balancing open inquiry with societal needs.

The geosciences: modes of inquiry and challenges

• Earth sciences often study large, complex, real-world systems where controlled lab experiments are not feasible for all questions.
• Traditional lab-style experimentation (controlling a single variable) is sometimes impractical in geosciences due to big spatial scales and long time scales.
• Alternative approaches in geology/earth science include:
  • Observing changes over time: measuring a variable (e.g., river discharge) and tracking how it changes; use the order of events to constrain causality (e.g., a rainstorm preceding a rise in river flow).
  • Assessing the rate of change to gauge influence strength (rapid changes imply stronger influence).
  • Using modern analogs: comparing present-day processes to past geological features (e.g., wind or wave action producing ripple marks in sandstone today to interpret past environments).
  • Examining spatial variation: comparing patterns across latitude, distance from coast, or other spatial factors to understand how variables covary.
  • Modeling approaches:
  • Physical models (e.g., stream tables) provide controlled, manipulable analogs of natural environments.
  • Computer models simulate complex systems with math and code to test how changes in drivers (e.g., CO₂, slope) affect outcomes.
• Multimethod, collaborative approaches are common: combining data collection, physical models, and computer models to build confidence in explanations by converging on the same conclusions through different methods.
• Important concepts:
  • Order of events constraining causality: e.g., if a rainstorm occurs before a rise in flow, it could be a causal driver; if the order is reversed, it cannot.
  • Covariation and correlation across space and time help identify potential drivers.
  • Multiple working hypotheses (MWH): maintaining several plausible explanations in parallel to avoid bias and to test which is best supported by data.
  • Predictions should be generated from each hypothesis to guide further data collection and testing.
• Example of a practical method in the geosciences: the use of a stream table to simulate river responses to changes in flow or slope.

Field trip example: Death Valley racetrack phenomenon

• Location and setup:
  • The racetrack is a flat, light-tan center area with steep topography on the sides (contour lines close together indicate steep terrain; farther apart indicates flatness).
  • Observers move from an aerial view to the surface to examine what appears on the ground.
• Observations from the site (descriptions and observations vs interpretations):
  • The racetrack surface appears dry with polygonal cracks; rocks and dark boulders sit on the pale ground; surrounding mountains are darker.
  • Notable features observed in photos include tracks or indentations leading toward or away from rocks, cracks in the ground, lack of vegetation, and a generally dry surface.
  • Distinguishing observations from interpretations is emphasized: observations describe what is seen; interpretations infer processes (e.g., “rocks have moved” is observational only if directly observed, whereas “the rocks moved due to wind” is an interpretation).
• Tools and logistics for the class activity:
  • iClicker setup and classroom logistics for collecting observations via a short-answer poll (word clouds, descriptions).
  • Emphasis on using a campus network (CSUNet) and enabling location permissions for iClicker Cloud.
  • Observational activity followed by group discussion to generate hypotheses about the tracks.
• Initial observations from student groups:
  • Rocks on a dry, cracked surface with no visible vegetation; surrounding steep ridges; tracks and paths around rocks; color differences (tan ground, darker rocks).
  • General interpretation issues: distinguishing direct observations from inferred processes (e.g., rock movement, wind-driven tracks).
• Generating hypotheses about the tracks (two or more groups produced multiple options):
  • Potential causes suggested:
  • Rocks moved by human activity or people moving rocks.
  • Glacier movement in the past pushing rocks across the track.
  • Rocks transported by wind pushing along the surface.
  • Seasonal or moisture-related lubrication allowing sliding (e.g., moisture or ice enabling movement).
  • Earthquakes causing shaking and sliding.
  • The instructor highlights that multiple working hypotheses are a common geoscience practice to avoid bias and ensure robust data collection.
• Testing hypotheses and making predictions:
  • Students discuss additional data, experiments, or models that could test the hypotheses (e.g., time-lapse monitoring, moisture measurements, wind records).
  • A humorous suggestion to use a leaf blower or a blow dryer to test rock movement illustrates how one might experimentally test forces driving motion, though practical and ethical considerations apply in real experiments.
• Key teaching point: multiple working hypotheses help prevent data collection bias and broaden the search for explanations; predictions and experiments should differentiate between competing hypotheses.
• Summary takeaways from the racetrack exercise:
  • The phenomenon involves relatively slow rock movement within a ponded, ice-containing environment.
  • Movement is not necessarily driven only by extreme weather; it can occur under comparatively mild conditions when a shallow pond of meltwater and ice interacts with sun and breeze.
  • The racetrack case demonstrates how diverse data sources (qualitative observations, GPS measurements, weather data, time-lapse imagery) support a multi-method approach to understanding a geoscience puzzle.

Data collection and analyses used in the racetrack study

• Data streams collected:
  • GPS tracking inside rocks to quantify movement (embedded in rocks, 15 rocks deployed; observed long-term movement after the pond formed).
  • Weather station data to correlate movement with meteorological conditions (wind strength, temperature, possibly precipitation).
  • Time-lapse imagery to visualize rock movement and the dynamics of the interaction between rocks, ice, and water.
  • Direct observation during field-time when rocks moved, including periods when rocks were stationary and events when movement occurred.
• Quantitative findings highlighted in the video excerpt:
  • Rocks moved very slowly: about $2 ext{ to } 5 ext{ m/min}$ when movement occurred.
  • Movement events documented across a pond lifespan of about $2.5 ext{ months}$, with rocks moving at least $4$ times during that period.
  • Some rocks moved multiple times; GPS data showed movement over distances of up to hundreds of feet (e.g., rock trails observed exceeding $700 ext{ ft}$).
  • Ice panels, even when only thin (often no thicker than a windowpane), combined with a shallow pond and light winds, could push rocks along the surface.
• Key methodological takeaways:
  • A multipronged data approach (GPS, weather data, time-lapse imagery, direct observation) provides a robust basis for explaining the phenomenon.
  • Observations of the environment (presence of a pond, floating ice, sunlit surface) are critical to interpreting movement mechanics.
  • The movement is slow and occurs under a fairly narrow set of environmental conditions, which explains why it may not have been observed previously.
• Geological significance and broader implications:
  • Demonstrates how small-scale, subtle forces acting over time can cause noticeable geological movement.
  • Highlights the importance of long-term monitoring and opportunistic data collection when natural phenomena occur infrequently.
  • The racetrack case provides a concrete example of how to test hypotheses with real-world data and integrate multiple lines of evidence to support or refute proposed mechanisms.

Which methods were used in the racetrack study and how were they useful?

• Time-based observations:
  • Tracking movement as it happened provided direct evidence of rock motion and allowed correlation with environmental conditions.
• Spatial model (corral/experimental setup):
  • The initial small-scale setup where two rocks were placed in a corral demonstrated whether ice or other forces could move rocks in a controlled setting; this served as a spatially explicit test of movement potential.
• Instrumented data collection:
  • Weather station measurements help determine whether wind, temperature, or moisture conditions align with rock movement events.
  • GPS units embedded in rocks provided precise quantitative measures of displacement and timing, enabling post-hoc analysis of movement patterns.
• Visual data:
  • Time-lapse imagery offered qualitative confirmation of rock movement and the sequence of events during pond formation, ice accumulation, and movement.
• Integration and interpretation:
  • Combining data streams allowed researchers to form a coherent explanation: movement occurred due to a combination of a shallow pond, floating ice, light winds, and solar heating, rather than a single extreme weather factor.
• Notable outcome: the study used a multi-method approach to triangulate the mechanism behind the racetrack phenomenon, reinforcing confidence in the conclusions.

Ethical, practical, and educational implications discussed

• Scientific practice and culture:
  • Feedback and critique are essential parts of rigorous science, informing improvements to hypotheses and methods.
  • Field-based science requires careful consideration of safety, logistics, and ethics when conducting experiments or observational studies in remote or extreme environments.
• Education and student participation:
  • Tools like iClicker facilitate active participation, quick data collection, and engagement during lectures.
  • Distinctions between observations and interpretations are emphasized to cultivate scientific literacy and critical thinking.
• Policy relevance and communication:
  • Scientific findings can influence public policy (e.g., environmental management, resource use, hazard assessment).
  • Clear communication of uncertainties and the iterative nature of science helps policymakers understand the strength and limits of scientific conclusions.

Quick recap of key terms and concepts

• Multiple Working Hypotheses (MWH): maintain several plausible explanations to avoid confirmation bias and guide data collection.
• Observations vs Interpretations: observations are what is directly seen; interpretations are explanations inferred from those observations.
• Causality and order of events: establishing which event precedes another helps identify plausible causal drivers.
• Covariation and correlation: patterns in space and time that suggest potential drivers or relationships.
• Modes of inquiry in geoscience: time-series observations, modern analogs, spatial variation, physical models, computer models.
• Data convergence: using multiple independent methods to reach the same conclusion increases confidence.
• Paleogeography and analog reasoning: using present-day processes to infer past environmental conditions.
• Scale and complexity: earth systems operate over large spatial and temporal scales; field studies must adapt methods accordingly.

Looking ahead: next class focus and logistics

• Next class topic: scale, focusing on large spatial and temporal scales, and constructing a timeline of Earth's history.
• Administrative reminders:
  • Complete the intro survey and establish class norms.
  • Prepare for an assignment that involves videos and readings; plan for an upcoming assessment.
• Encouragement: engage with the material, bring observations, and think about how multiple methods can be used to test hypotheses in real-world settings.