Bibliographic record
Abstract
Abstract Computer-based learning tools are becoming more prevalent in classrooms from elementary school to higher education. The potential value of interactive learning tools is particularly high in geoscience education. Students can benefit from interactive tools that allow them to explore different processes in one-dimensional (1-D), two-dimensional (2-D), and three-dimensional (3-D) space. Traditionally, geoscience education has relied on laboratory exercises to provide students with the opportunity to explore dimensionality. In this chapter, we introduce Visible Geology, an innovative web-based application designed for geoscience education. Visible Geology enables visualization of geologic structures and processes through the use of interactive 3-D models. As Visible Geology has been designed from a student-centric perspective, it has resulted in a simple and intuitive interface, allowing students to creatively explore concepts. We present a case study of a large first year class at the University of British Columbia, and show the utility of Visible Geology in teaching geoscience concepts of relative dating and cross-cutting relationships. The ease of use of the software for this assignment, including automatic grading, made this tool practical for deployment in classrooms of any size. The outcome of this type of large-scale deployment is that students, who would normally not experience a lab exercise, gain exposure to 3-D thinking. The level of ownership and interactivity inherent in Visible Geology encourages engagement, leading learners to practice visualization and interpretation skills and discover geologic relationships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.007 |
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".