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Smart paddleboard and other assistive veyances

2023· article· en· W4375854256 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIoT-based Smart Home Systems
Canadian institutionsJoint Attosecond Science LaboratoryUniversity of Toronto
Fundersnot available
KeywordsPaddleThrottleAutomotive engineeringTension (geology)Controller (irrigation)SimulationEngineeringFLEXComputer scienceAeronauticsMechanical engineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

As a contribution to the new field of WaterHCI (Water-Human-Computer Interfaces), we proposed and developed a smart SUP (Stand-Up Paddleboard) to assist a person with a disability (shoulder + back injury) to continue paddling and cross-country swimming (pulling a paddleboard to carry cargo while swimming). The paddleboard technology consists of two thrusters (motorized propellers) driven (1) in proportion to the flex of a paddle, to maintain the same feeling as normal paddling but with easement on shoulder strain, or (2) in proportion to the tension of a tow line attached to a waist strap around a swimmer’s waist. We also propose a controller for controlling a throttle using paddle flex or the tension of a pull cord. We also propose the use of our throttle control technology in transporting the paddleboard by way of a pulled wagon or an electric cargo bicycle (loaded up with the paddleboard and related supplies), at times when it is necessary to push it up steep hill. The wagon has a pull cord similar to the paddleboard, and the bicycle consists of a handlebar equipped with force sensors to provide the similar effect to pulling the paddleboard or wagon.

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.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.216
Teacher spread0.201 · 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

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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