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Record W2904574873 · doi:10.5334/jors.230

Turtle Sport: An Open-Source Software for Communicating with GPS Sport Watches

2018· article· en· W2904574873 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Open Research Software · 2018
Typearticle
Languageen
FieldComputer Science
TopicVideo Surveillance and Tracking Methods
Canadian institutionsUniversité de MontréalInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsComputer scienceGlobal Positioning SystemSoftwareTurtle (robot)JavaUploadOpen sourceOpen source softwareWorld Wide WebComputer securityOperating systemArtificial intelligence

Abstract

fetched live from OpenAlex

The aim of this article is to introduce an open-source software—Turtle Sport—that is capable of automatically importing the GPS traces of several types of GPS sport watches (Garmin, Polar, Suunto, Timex, TomTom, etc.) or of importing a number of GPS files. The GPS data are also uploaded locally to the researcher’s computer workstation, and not to Cloud, which may raise important ethical issues. Turtle Sport also allows users to: manage a number of users; visualize the traces and statistics for the races; and export the traces to external files (GPX, KML). Developed in Java, Turtle Sport is a stand-alone, multiplatform (Windows, Mac and Linux) and multi-language (11 languages supported) application. The software is available under GNU LGPL 2.1 Licence on SourceForge (https://sourceforge.net/projects/turtlesport/). Funding statement: The publication of the paper was supported by the Canada Research Chair in Environmental Equity (950-230813).

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)0.017
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.746
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0030.004
Open science0.0130.004
Research integrity0.0000.001
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 head0.188
GPT teacher head0.461
Teacher spread0.272 · 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