GloVis as a Resource for Teaching Geographic Content and Concepts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Abstract Teachers of geography and related topics across a range of educational levels benefit from convenient access to graphic materials that illustrate key geographic information and concepts. GloVis (Global Visualization Viewer) is an online tool designed by the U.S. Geological Survey to facilitate archival searches for Landsat and related imagery. GloVis preview images provide a resource for teachers who wish to illustrate geographic concepts in the context of landscapes local to their institutions. This article introduces instructors to GloVis and offers classroom examples illustrating a variety of applications. Specific examples, including Virginia's physiographic provinces, regional phenological changes, New Orleans observed before and after Katrina, surface-mined landscapes in eastern Kentucky, and suburban sprawl along Virginia's I-95 corridor, illustrate the capabilities of this resource. Key Words: teachingGloVisremote sensinglandscapeLandsatgeography Acknowledgement The author acknowledges the contributions of three anonymous reviewers, and the comments provided by Mr. Tim Beckmann, Science Applications International Corporation, USGS/EROS Center for Earth Resources Observation & Science, Sioux Falls, South Dakota. R. Sivanpillai, R. W. Morrill, and L. W. Carstensen were generous with respect to the time they devoted to review of the manuscript and the useful suggestions they offered. James B. Campbell, Ph.D., is professor of geography at Virginia Tech in Blacksburg, Virginia, USA, where he teaches remote sensing, quantitative methods, and geomorphology. He has worked closely with students and faculty in allied fields, including forestry, geology, agronomy, and environmental sciences. Since 1997 he has served as codirector of Virginia Tech's Center for Environmental Applications in Remote Sensing (CEARS). He is author of several books, including Introduction to Remote Sensing, now in its fourth edition, widely used at universities in the U.S., Canada, and overseas, as well as numerous technical articles.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it