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Record W4410441807 · doi:10.1002/lob.10707

From Headwaters to the Sea: The Journey of Creating an Immersive Museum Exhibit

2025· article· en· W4410441807 on OpenAlexaffabout
B. Rodriguez-Cardona, Pedro M. Barbosa, Pascal Bodmer, Eve‐Lyne Cayouette Ashby, Michaela L. de Melo, Paul A. del Giorgio, Mariana Peifer Bezerra, Sara Soria‐Píriz

Bibliographic record

VenueLimnology and Oceanography Bulletin · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsEspace pour la vieUniversité du Québec à Montréal
Fundersnot available
KeywordsOceanographyGeology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.228
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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