How’s\nthe Air Out There? Using a National Air\nQuality Database to Introduce First Year Students to the Fundamentals\nof Data Analysis
Bibliographic record
Abstract
Chemistry is increasingly data centric and the undergraduate\ncurriculum\nneeds to adjust to keep up. To address this, we created the <i>Air Quality Activity</i>, a new first-year undergraduate activity\nwhere students use Microsoft Excel to analyze a unique subset of atmospheric\nozone (O<sub>3</sub>) and nitrogen dioxide (NO<sub>2</sub>) measurements\nfrom the Canadian National Air Pollution Surveillance (NAPS) program.\nThrough this activity students develop their numeracy, graphicacy,\nand proficiency with Excel. Moreover, students are equipped with a\nfoundational approach to data analysis they can leverage throughout\ntheir studies. To make this activity possible, we developed an open-source\nwebbook detailing pertinent Excel operations for first-year students,\nand an interactive web-app for the generation, distribution, and exploration\nof NAPS data. Students were excited by the analysis of real-world\nchemical phenomena in comparison to traditional first-year lab exercises\nand appreciated their acquired Excel skills. The <i>Air Quality\nActivity</i> is readily adaptable for both virtual and in-person\nimplementation, entirely open-source, and readily deployable at any\ninstitution wishing to teach data analysis in a chemistry context.
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 | 0.000 | 0.005 |
| 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 | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.052 | 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; both teacher heads agree on what is shown here.
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".