MétaCan
Menu
Back to cohort
Record W2946984510

Design of A Monitoring System to Track Dragon Boat Performance

2017· article· en· W2946984510 on OpenAlexaff
Aassem Askari Askari

Bibliographic record

VenueStudent Research Proceedings · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsMacEwan University
Fundersnot available
KeywordsPaddleUnisonTrack (disk drive)AeronauticsWatercraftEngineeringComputer scienceMarine engineeringMechanical engineeringAcoustics
DOInot available

Abstract

fetched live from OpenAlex

Dragon boat racing is a sport which has originated in ancient China, and is one of the fastest growing sports in the world. A typical team consists of 20 paddlers working in unison to propel a boat forward in a race against other teams. The main aim of this project is to develop a sensor apparatus that can be mounted onto a paddle to collect data for each paddler's contribution to the overall movement of the boat - assessing the paddler's applied force in the water and their paddle's position through the stroke profile. The collected data can then be analyzed and compared against other paddlers to determine the most efficient course of action. Discipline: Physics 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.628

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 communication0.0000.001
Open science0.0020.002
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 head0.220
GPT teacher head0.425
Teacher spread0.205 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueStudent Research ProceedingsSame topicWater Quality Monitoring TechnologiesFrench-language works237,207