Crafting Glacial Narratives: Virtual Exploration of Alpine Glacial and Periglacial Features in Preston Park, Glacier National Park, Montana, USA
Bibliographic record
Abstract
Virtual learning environments (VLEs) in physical geography education offer significant potential to aid students in acquiring the essential skills for the environmental interpretation of glacial and periglacial environments for geoscience careers. Simulated real-world field experiences aim to help the student evaluate landscapes for natural hazards, assess their intensity, and translate and communicate this information to various stakeholders in human systems. The TREE-PG framework and VRUI model provide a philosophical and practical foundation for VLE architects, aiming to cultivate students’ knowledge, skills, and identity as geoscientists, specifically as physical geographers and geomorphologists. These frameworks emphasize the importance of translating scientific knowledge from physical features into engaging, accessible online lessons, exemplified by landscapes like those in Glacier National Park, Montana. Open-source software and open educational resources (OERs) can broaden access and incorporate diverse perspectives in these experiences, which are necessary to address the impacts of vulnerable communities to global deglaciation. Designing and creating virtual proxies of field-based education may help address issues associated with inclusion and belonging within geoscience disciplines to connect all students with dynamic physical environments beyond the classroom. Ethical AI approaches and discipline-specific repositories are needed to ensure high-quality, contextually accurate VLEs. AI’s tendency to produce output necessitates using domain-specific guardrails to maintain relevance and precision in virtual educational content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".