Is That a Peak? A Course-Based Research Experience That Has Students Performing a Nontargeted Analysis of Dust and Consumer Products
Bibliographic record
Abstract
Modern analytical environmental chemistry has increasingly moved away from traditional targeted chemical monitoring and toward a more nontargeted, big data approach. In this activity, students use freely available software to perform a nontargeted analysis of high-resolution mass spectrometry data from real indoor dust and consumer product samples. Students collect supporting evidence within the dataset to reduce uncertainty and contextualize their results within the field of environmental chemistry using primary literature. There is also an emphasis on data analysis as a core component of modern chemical research. This activity was delivered twice over the course of two years, first online during the initial wave of COVID-19, then in person the subsequent year, whereupon R coding was introduced to semiautomate the detection of candidate compounds. Student feedback was strong, with 96% of students indicating moderate to high satisfaction with the research component of the activity after the second iteration. Sufficient resources and sample data are provided for instructors to deliver this activity without needing access to a high-resolution mass spectrometer.
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.
How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".