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Record W3166695804 · doi:10.5194/egusphere-egu21-6367

The Virtual Water Gallery: a collaborative science and art project

2021· article· en· W3166695804 on OpenAlexaffabout
Louise Arnal, Martyn Clark, Stacey Dumanski, John W. Pomeroy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsVirtual waterExcellenceFutures contractPublic engagementSociologyPublic relationsEngineering ethicsPolitical scienceBusinessWater scarcityEngineeringWater resourcesEcology

Abstract

fetched live from OpenAlex

Water is life and so water-related challenges, such as droughts, floods and water quality degradation, affect everyone. Conceptualizing water-related environmental and social problems in novel ways, with engagement between the public and science researchers, may lead to new and more comprehensive solutions to complex problems. A society that makes decisions informed by science and science that approaches problems in a transdisciplinary manner are key elements in finding creative and holistic solutions to the water-related challenges we all face. We believe that art can help co-establish new social norms to help us grasp and tackle water-related challenges in a more holistic manner. The Virtual Water Gallery* is a science and art pilot project funded by Global Water Futures (GWF). GWF is a University of Saskatchewan-led research program that is funded in part by the Canada First Research Excellence Fund. Its overarching goal is to deliver risk management solutions, informed by leading-edge water science, to manage water futures in Canada and other cold regions where global warming is changing landscapes, ecosystems and the water environment. Launched in Summer 2020, the Virtual Water Gallery aims to provide a safe, inclusive and collaborative space for fully open discussions between scientists, artists, and a wider public, to explore past, present and future water challenges. As part of this pilot project, 13 artists were paired with teams of GWF scientists to co-explore specific water challenges in various Canadian ecoregions and river basins, including the Arctic, the mountains, boreal forests, prairies, farmlands, lakes, rivers, and communities. These collaborations are leading to the co-creation of science and art pieces which will be exhibited online on a Virtual Water Gallery. By making this online exhibition accessible to a global audience, we hope that the co-created art pieces will open creative and informative discussions about urgent water challenges to a wider audience via the gallery space. *More information about the Virtual Water Gallery on the GWF webpage: https://gwf.usask.ca/outreach/virtual-water-gallery.php

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.057
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0090.007
Scholarly communication0.0120.006
Open science0.0030.016
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0570.010

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 head0.009
GPT teacher head0.211
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2021
Admission routes2
Has abstractyes

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