From Headwaters to the Sea: The Journey of Creating an Immersive Museum Exhibit
Bibliographic record
Abstract
Abstract We describe the multi‐year journey of a group of researchers co‐creating a museum exhibit with the Biosphère an environmental museum of the City of Montréal. The collaboration resulted in an immersive experience where visitors can dive into an aquatic continuum and learn about function, ecology, chemistry, and the roles of the various aquatic ecosystems within a watershed and the broader landscape. Here we share the details of this journey from idea conception, the process and challenges of collaborating with different working teams, and some of the lessons learnt on teamwork, collaborations, and science communication. Challenges included composing with an eclectic group of collaborators with heterogeneous expertise and approaches, effectively communicating and interacting with diverse partners, transposing and adapting scientific concepts and ideas in order to reach a general audience, overcoming language and cultural and disciplinary barriers, among others. These challenges turned into opportunities that allowed our group to develop leadership skills and self‐trust, skills to more effectively collaborate and partner in positive and creative ways, and approaches to more successfully communicate our science, and allowed us to contribute positively to society at a time when this is needed more than ever.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".