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Record W6969248981 · doi:10.5683/sp3/mgqybr

Data set of "Smart" Brace Validation Study

2023· dataset· en· W6969248981 on OpenAlexaff

Bibliographic record

VenueBorealis · 2023
Typedataset
Languageen
FieldComputer Science
TopicLogic, programming, and type systems
Canadian institutionsYork University
Fundersnot available
KeywordsInertial measurement unitBraceTreadmillSession (web analytics)Motion captureData setSet (abstract data type)

Abstract

fetched live from OpenAlex

This data set was collected to validate a 'smart' knee brace with an IMU embedded on the thigh and shank area against a reference motion capture system (Vicon). There were 10 participants total and each participant came into the lab for 2 sessions, each on separate days. For each session, participants completed three trials of 2-minute treadmill walking at their preferred walking speed, three trials of 15 squats to parallel, three trials of 10 sit-to-stand on a chair that was about knee level, three trials of 15 total alternating lunges, and three trials of 2-minute treadmill jogging at their preferred speed, all in that order. 10 squats and 10 lunges were done for some participants' first sessions, but then did 15 squats and lunges in the second session (a .txt file will be included for each participants' session to specify). This dataset only contains the IMU data.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0030.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0160.035

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 head0.093
GPT teacher head0.331
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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