Earth Science Education #7. GeoTrails: Accessible Online Tools for Outreach and Education
Bibliographic record
Abstract
As geoscientists, we must prioritize improving our ability to communicate science to the public. Effective geoscience communication enables communities to understand how geological processes have shaped our planet and make informed decisions about Earth’s future. However, geoscience research outputs have traditionally been published in peer-reviewed journals and presented at academic conferences. Consequently, essential information about local geology is rarely available in accessible, open access, and engaging formats. Here, we propose virtual field trips, or ‘GeoTrails’, as a possible solution to address the disconnect between geoscience research and public knowledge by improving our communication to the public. This initiative is largely driven by undergraduate students, who identify points of geological interest along selected hiking trails, write concise descriptions derived from scientific sources (e.g. longer peer-reviewed articles and government reports), and collect field data (e.g. 3-D LiDAR models, drone photography) to illustrate the characteristics of these geological features. The goal of the project is to communicate the importance of local geology on our environment and to raise awareness of how changing climates could affect us in the future; this information can empower communities to make better, more informed planning decisions. The creation of GeoTrails along the Niagara Escarpment offers a promising strategy to highlight the role of geoscientists and to engage the public in our ongoing research that aims to showcase Canada’s geoheritage.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.394 | 0.177 |
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".