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Record W2945394642

Development of a Paddle Sensing System for Studying Paddle Dynamics for Dragon Boating

2018· article· en· W2945394642 on OpenAlexaff
Darren Berg

Bibliographic record

VenueStudent Research Proceedings · 2018
Typearticle
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsMacEwan University
Fundersnot available
KeywordsPaddlePhoneMobile phoneComputer scienceEngineeringSimulationTelecommunicationsMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

Dragon Boat racing is a team sport that involves 20 paddlers in coordination racing against other teams over distances from 200 m to 2000 m. With some races being won by mere seconds, a small desynchronization of the team could mean the difference between winning and losing. Therefore being able to collect data on a rower’s stroke pattern is of great benefit to competitive teams. Using this data a coach can examine stroke profiles for all team members, and address problems as they arise during practice. The objective of this project was to further develop a paddle sensing unit (PSU) that can be used to monitor the paddling patterns of a rower. The improvements were to make the original PSU smaller, mountable to a paddle, and use wireless communication for data transfer. This involved the development of a circuit, designing a housing for the circuit on the paddle, and developing a phone application to collect the transmitted data. Discipline: Physical Sciences Faculty Mentor: Dr. Orla Aaquist

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0000.000

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 head0.096
GPT teacher head0.367
Teacher spread0.271 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2018
Admission routes1
Has abstractyes

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