Introducing a Novel Course-Based Undergraduate Research Experience Using Duckweed as a Model System
Bibliographic record
Abstract
Course-based undergraduate research experiences (CUREs) provide a scalable model for engaging students in authentic scientific inquiry, bridging core biological concepts with real-world environmental applications. We introduce a new CURE lab tailored for introductory biology students at the undergraduate level, utilizing duckweed as a model organism to investigate ecological interactions and environmental management. Our paper presents a curriculum that engages students in hands-on research with a focus on duckweed's role in ecosystem dynamics, pollutant remediation, and its potential as a bioresource, along with scientific results from student projects that serve as tangible examples of the curriculum's outcomes. Through experimentation, students explore how duckweed can be applied to address real-world environmental challenges, utilizing advanced laboratory techniques and data analysis tools. Successfully implemented with 192 students across three semesters at our institutions, this CURE lab has produced reliable duckweed growth data with high reproducibility. This curriculum addresses the gap between traditional laboratory exercises and authentic research experiences through introducing opportunities to conduct reproducible experiments, analyze real data, and communicate scientific findings in meaningful contexts.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".