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Record W4403307483 · doi:10.1145/3665463.3678785

Hydrone: Bringing Interactivity to Small Water Features

2024· article· en· W4403307483 on OpenAlexaff
Mohsen Ensafjoo, Alexander Bakogeorge, Chau Nguyen, Alexander Verni, Finch Gabriel Assadoullaev, Vivian Fung, David McFarlane, Paul Dietz, Fanny Chevalier, Ali Mazalek

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of TorontoToronto Metropolitan University
Fundersnot available
KeywordsInteractivityComputer scienceHuman–computer interactionWorld Wide Web

Abstract

fetched live from OpenAlex

There is a deep connection between humans and water that evokes emotions ranging from the calm of a babbling brook to the terror of a terrible storm. While water features such as fountains are often used architecturally to drive the feeling of a place, most such examples are passive, involving little participation from the guest. We present the Hydrone, a small, autonomous watercraft that can transform small water features into personal, playful, and interactive experiences. This paper describes our initial explorations in creating connection between a land-bound guest and a Hydrone in the water. Our research-through-design (RtD) process explored and expanded the palette of expressive capabilities for the Hydrone through a series of prototype designs. These include the use of water jets and locomotion to evoke emotion, as well as physical design and aesthetic considerations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.565
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.014
GPT teacher head0.271
Teacher spread0.257 · 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 designBench or experimental
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

Citations2
Published2024
Admission routes1
Has abstractyes

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