<i>JCE</i> Classroom Activity #110: Artistic Anthocyanins and Acid–Base Chemistry
Bibliographic record
Abstract
Art and science are sometimes viewed as opposing subjects, but are united in many ways. With an increased awareness of the benefits of interdisciplinary studies in education, it is desirable to show students how different subjects impact one another. Visual arts are greatly connected to chemistry in several ways. Pigments are usually synthetically produced to conjure all the colors of the rainbow for creating many great masterpieces. However, these hues were originally derived from naturally occurring minerals and plants. Students can still paint a “green” or environmentally friendly picture, using colors obtained without synthesis. The fun procedure outlined here illustrates how red cabbage juice, along with other types of produce, can be used to prepare an inexpensive canvas that can be transformed into works of art while using acids and bases to modify the chemical structure of the anthocyanin pigment within the produce. Using this hands-on classroom activity, students will understand the benefits of using natural pigments, investigate how colors can be manipulated, make color gradients, and explore how using different media can affect an individual’s artwork. Importantly, students will also develop understanding of the interconnection between science and art.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.384 | 0.133 |
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".