Modeling Ice Patch Location via GIS Analysis of Topography - Short Paper
Bibliographic record
Abstract
In high altitude and high latitude regions of North America, researchers have documented ancient organic cultural remains emerging from patches of permanent ice and snow known as ice patches (Dixon, et al. 2005; Hare, et al. 2004; Lee, et al. 2006; Vanderhoek, et al. 2007). Collections of rare and well-preserved organic artifacts from these vanishing archaeological contexts provide new insights about the interactions between hunter-gatherers and mountainous environments. Ongoing ice patch research in central Alaska and the Canadian Yukon has demonstrated that ice patches tend to be located on north-facing slopes and high elevation flats above such slopes. In Alaska, ice patches occur at different elevations in different parts of the state, and many exhibit dramatic ablation in response to climate change. Their formation and survival is dependent on a number of interactive variables including precipitation, slope, aspect, elevation, albedo, basal topographic characteristics, prevailing winter wind direction, and snow catchment area . Methods for ice patch discovery include fixed wing aerial survey, analysis of satellite imagery and other remote sensing data, and helicopter-supported pedestrian survey of potential ice patches. Although aerial survey and remote sensing analyses can reduce the potential survey universe, it is necessary to conduct pedestrian survey in order to determine whether culturally modified objects are exposed at the surface of an ice patch. Because helicopter time is extremely expensive and survey areas may encompass hundreds of square miles of mountainous terrain, it is imperative to accurately select areas with the highest potential for ice patch preservation. This research develops a predictive model for ice patch location using slope, aspect, elevation, and solar radiation values derived from a 23m ASTER digital elevation model (DEM). Topographic parameters for five Alaskan ice patches were used to create bounding values for environmental contexts that can support permanent ice. The model was applied to unsurveyed areas of Katmai National Park and focused the survey area on 1,083 km 2 , or 6.5% of the Park’s 16,563 km 2 area. Ground-truthing in 2011 (a low-melt summer) confirmed the presence of permanent ice in these areas, but failed to locate ancient cultural remains. Dixon, E. J., W. F. Manley and C. M. Lee 2005 The emerging archaeology of glaciers and ice patches: Examples from Alaska's Wrangell-St. Elias National Park and preserve. American Antiquity 70(1):129-143. Hare, P. G., S. Greer, R. Gotthardt, R. Farnell, V. Bowyer, C. Schweger and D. Strand 2004 Ethnographic and archaeological investigations of alpine ice patches in southwest Yukon, Canada. Arctic 57(3):260-272. Lee, C. M., J. B. Benedict and J. B. Lee 2006 Ice Patches and Remnant Glaciers: Paleontological Discoveries and Archaeological Possibilities in the Colorado High Country. Southwestern Lore 72(1):26-43. Vanderhoek, R., R. M. Tedor and C. E. Holmes 2007 Cultural Materials Recovered From Ice Patches In The Denali Highway Region, Central Alaska, 2003-2005. Alaska Journal of Anthropology 5(2):185 - 200.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".