Developing scientific literacy with a cyclic independent study assisted CURE detecting SARS-CoV-2 in wastewater
Bibliographic record
Abstract
The COVID-19 pandemic has exposed a high level of scientific illiteracy and mistrust that pervades the scientific and medical communities. This finding has proven the necessity of updating current methods used to expose undergraduates to research. The research in traditional course-based undergraduate research experiences (CUREs) is limited by undergraduate time constraints, skill level, and course structure, and consequently it does not attain the learning objectives or the high-impact, relevant studies achieved in graduate-level laboratories using a cyclic trainee/trainer model. Although undergraduate independent study (ISY) research more closely matches the structure and learning objectives of graduate-level research, they are uncommon as professors and universities typically view them as a significant time and resource burden with limited return. Cyclic independent study-assisted CUREs (CIS-CUREs) combine many positive aspects of ISY graduate-level research, and CUREs by pre-training ISY research lead to facilitate CURE proposal and project semesters in a cyclic model. The CIS-CURE approach allowed undergraduate students at Stetson University to perform and disseminate more rigorous, involved, long-term, and challenging research projects, such as the surveillance of SARS-CoV-2 in wastewater. In doing so, all students would have the opportunity to participate in a high-impact research project and consequently gain a more comprehensive training, reach higher levels of research dissemination, and increase their competitiveness after graduating. Together, CIS-CUREs generate graduates with higher scientific literacy and thus combat scientific mistrust in communities.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".