Introducing a Novel Course-Based Undergraduate Research Experience Using Duckweed as a Model System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it