DNA at the whim of the water: environmental DNA as a course-based undergraduate research experience
Bibliographic record
Abstract
Course-based undergraduate research experiences (CUREs) increase student access to high impact research experiences. CUREs engage students in the scientific process by learning how to pose scientific questions, develop hypotheses, and generate data to test them. Environmental DNA (eDNA) is a growing field of research that is gaining accessibility through decreasing laboratory costs, which can make a foundation for multiple, engaging CUREs. This manuscript describes three case studies that used eDNA in an upper year undergraduate course. The first focusses on a systematic literature review of eDNA metadata reporting. The second describes the biomonitoring of brook trout in southern Ontario using eDNA. The third involves eDNA metabarcoding for freshwater fish detection in southern Ontario. Undergraduates were involved in the development and execution of experiments, scientific communication, the peer review process, and fundraising. Through this manuscript, we show the novel application of eDNA CUREs and provide a roadmap for other instructors interested in implementing similar projects. Interviews with seven students from these courses indicate the benefits experienced from taking these courses. We argue that the use of eDNA in CUREs should be expanded in undergraduate biology programs due to the benefit to students and the increasing accessibility of this technology.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".