Bioplastics in the General Chemistry Laboratory: Building a Semester-Long Research Experience
Bibliographic record
Abstract
We report here a second semester general chemistry laboratory project themed around chitosan-alginate bioplastics. With increasing awareness of plastic pollution in the environment and the awareness of the importance for materials which are either made from renewable resources or are biodegradable, this topic provides a relevant opportunity to engage students. The semester-long laboratory experience has students working in teams first to complete a series of core experiments which provide a foundational experience in preparing and testing chitosan-alginate bioplastics prior to developing and implementing a project in a direction of their own choosing. The benefit of these student directed research projects can include enhanced engagement and can allow development of skills such as experiment design, data collection and analysis, written and oral dissemination, and critical thinking. We describe here both the core module in bioplastics which has the potential to be incorporated as a self-contained module by interested parties along with the way in which this is expanded to incorporate the student directed projects.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".