Enhancing geological skills through tactile learning with interactive multi-layered three-dimensional printed geological models of southern Ontario.
Bibliographic record
Abstract
In the digital age we often forget about the immense value of tactile learning. In other words, learning by touching and moving (i.e., assembling and disassembling) physical objects. Here we introduce the newly developed three dimensional (3D) printed models created by the Oil, Gas, Salt Resource Library that provide an unprecedented regional 3D perspective of the subsurface geology of southern Ontario. These innovative models simplify data from thousands of borehole records that are part of a 3D digital model focusing on the Paleozoic bedrock in southern Ontario (Carter et al. 2021). The 4-layer multi-coloured 3D print models are highly simplified versions of the 52-layer digital models. Nevertheless, the physical model encapsulates numerous aspects of the basin stratigraphy, geometry, and topography. Piloting the use of 3D printed geological models in undergraduate university courses (future Geoscientists and Geologic Engineers) in the form of laboratory exercises has shown to successfully bridge learning gaps in regard to conceptualizing spatial features (i.e., lateral extent, vertical thickness, and aspect of geologic layers), temporal context (i.e., relative age and preserved or missing layers) and economic resource formations (i.e., salt, water, oil, gas). The 3D print models also help students connect field observations with geologic interpretations (i.e., surficial mapping, subsurface borehole data to 3D surfaces, geometric shapes) as well as relating subsurface geology to surface features (i.e., landforms like escarpments and location of resources). In this workshop we will demonstrate the use of 3D printed models needed to bridge vital gaps in knowledge between real digital data and everyday life, applicable to the Geosciences. And then ask for help reflecting upon the use and impact of 3D printed models in other disciplines. Please bring your own device (smartphone, laptop, tablet) so you can experiment with resources and explore potential avenues to improve teaching and learning.
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 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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".