Using Open Science Tools to Teach Environmental Sciences
Bibliographic record
Abstract
Open science, work and knowledge that are developed in full, offers critical resources that provide students with insights into the process of research in many fields. There are extensive opportunities within environmental sciences to incorporate open science into undergraduate level courses. There are seven major open science concepts that could be used to teach undergraduate environmental science courses that align with professional research activities, including open-access papers, pre-prints, open data, open-source software, published code, collaborative tools for version control, and open notebooks. Here, we assessed the use of these open science concepts in connection to the European Union pillars of open science, outlining key benefits, challenges, and how these tools can be used in undergraduate environmental science courses. Specifically, these tools support a framework for open science structured around eight pillars, providing incentives to collaborate, enhancing transparency and openness, and promoting diversity and inclusivity. Collectively, these tools support teaching environmental science content as many of the skills gained directly relate to analyzing environmental topics and data while supporting transparency to collaborators and stakeholders. This provides learning opportunities including finding and reusing data, team collaboration, and reading and working with code. Further endorsing the use of open science in environmental science courses can enhance these courses as these tools align with professional research activities that are currently being used, including publishing data collected in labs, pre-print publishing capstone papers or lab reports, openly publishing code used for analysis, and publishing field notes.
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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 | 0.019 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.037 | 0.011 |
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".