Virtual Soil Monoliths: Blending Traditional and Web-Based Educational Approaches
Bibliographic record
Abstract
Since soil plays a crucial role in all aspects of global environmental change, it is essential that post-secondary institutions provide students with a strong foundation in soil science concepts including soil classification. The onset of information technology (IT) and web-based multimedia have opened new avenues to better incorporate traditional, static educational resources such as soil monoliths into post-secondary teaching and learning. The objective of this study was to develop an open access, web-based educational tool entitled “Virtual Soil Monoliths” (VSM) (http://soilweb.landfood.ubc.ca/monoliths/), based on a soil monolith collection at the University of British Columbia (UBC), Vancouver, Canada. With 197 monoliths, the UBC collection is the second largest of its nature in Canada, but due to poor storage and displays it has been underutilized in teaching. The VSM tool was developed by a team of scientists, instructional designers, IT specialists, and students and integrated into the Introduction to Soil Science course at UBC to support lectures and laboratory sections on parent material identification and soil classification. Student feedback indicated the VSM tool was helpful in facilitating student achievement of learning objectives related to basic soil classification and soil identification skills. Students used the VSM tool to complete assignments in the Introduction to Soil Science course, and students pointed out that the high-resolution monolith photographs were the most useful feature of the tool. This study provides a framework for incorporating inventory-type learning resources into an interactive teaching tool and a “living” educational resource that helps students grasp connections across disciplines.
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 | 0.002 | 0.003 |
| 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 | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".