The story of the Penobscot River Ecology Mural: A 10-step process for scientists to create public art
Bibliographic record
Abstract
Rivers are home to a wide variety of biota, including fishes, amphibians, reptiles, birds, freshwater mussels, aquatic insects, and microscopic organisms that fill unique niches to support broader ecosystem functions. While the general public may be aware of recreationally relevant biological life (e.g., fishes and insects to model flyfishing flies from), they may not be aware of more obscure yet important taxa. For example, freshwater mussels (Unionidae) improve water quality and clarity of rivers through filtration (Vaughn, 2018), which is vital to riverine ecosystems. Despite this importance, lack of public knowledge about freshwater mussels is the second biggest challenge to their management (Bouska et al., 2018). How do we best get the public to see value in underappreciated taxa to support their conservation? Public art installations can spread awareness about science effectively because they distill information for broader audiences, are eye-catching, and reach people through an emotional, aesthetic, and personal lens (Thompson et al., 2023). While invertebrates are generally viewed by the public with fear and aversion (Kellert, 1993) this can be ameliorated through interaction and learning, particularly for children (Miller et al., 2025). Through cross-disciplinary collaboration between artists, scientists and the local community, we created the “Penobscot River Ecology Mural,” a scientifically informed artwork featuring underappreciated taxa like mussels and insects. In this article, we will elucidate the story of how we successfully made this piece through 10 steps, which provide a blueprint for future endeavors.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".