The use of Gaming and Geodata Visualisation in Preparation for High Arctic Research Fieldwork
Bibliographic record
Abstract
Fieldwork is essential in many scientific disciplines, providing critical data for validating simulations and ground truthing. However, fieldwork is often costly, logistically challenging, and may require travel to remote or hazardous locations, necessitating thorough preparation and safety measures. Training in fieldwork skills begins at the university level, but proficiency is gained through experience over time. The University Centre in Svalbard emphasises Arctic fieldwork, integrating classroom instruction with on-site training. To enhance student preparation, we developed games and visualisation tools to help anticipate and manage fieldwork challenges. This article showcases several video games and outlines a guide for creating a video game using various data sources — satellite and aerial imagery, point clouds from remotely piloted aircraft systems (RPAS) — to explore Svalbard’s landscape. This versatile approach can be adapted to other regions or applications. Geographic Information Systems (GIS) are used to create thematic games, and we demonstrate visualisation techniques for teaching, publications, and outreach, including Virtual Reality (VR). Additionally, we explain how handheld LiDAR can scan and incorporate small local areas into the games, and how Micro-CT data can be used to explore microscale environments, such as a virtual flight through a snowpack. All methods use open-source products, or products with a limited, but free licence.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".