OCESE - Open-source Computing for Earth Science Education : Transforming Undergraduate Quantitative Teaching and Learning in UBC's Department of Earth, Ocean & Atmospheric Science Using Open Source Tools
Bibliographic record
Abstract
The 3-year OCESE (Open-source Computing for Earth Science Education) project aimed to embed and improve open-source resources and strategies for quantitative learning in the Earth, ocean, atmospheric, climate and environmental sciences. Outcomes of this project, carried out mainly within the Department of Earth, Ocean and Atmospheric Sciences (EOAS) at the University of British Columbia, include: 1) converting six courses from using MatLab to Python, 2) developing interactive dashboard apps for exploring data or quantitative concepts, 3) piloting several approaches to providing cloud computing for undergraduates, 4) documenting pedagogic practices, guidelines and tutorials to support continued enhancement of quantitative learning across all Earth science disciplines, and 5) delivering results as Open Education Resources. Overall, nineteen colleagues participated, thirteen students contributed time, talent and enthusiasm, and over 2000 students were impacted in more than fifteen courses spanning the diverse Earth, ocean and atmospheric curricula.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".