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Record W6944535213 · doi:10.21227/7gm9-mt95

Phantom Premium 1.5A Haptic Device

2022· dataset· en· W6944535213 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

VenueIEEE DataPort · 2022
Typedataset
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsImaging phantomTask (project management)Set (abstract data type)Haptic technologyTracking (education)TrajectoryLine (geometry)

Abstract

fetched live from OpenAlex

This is a data set resulting from the collaboration of the user and Phantom Premium $1.5$A Haptic Device, Geomagic Inc. Five right-handed users performed the task of tracking squiggly line in 6 trials. Since there is a significant difference between the performance of the trials done by the dominant hand and with ones performed by the non-dominant hand, we consider trajectories of the dominant hand of the users as expert data and trajectories coming from the other hand as novice data. This assumption is due to the fact that our dominant hand is fully trained to skillfully perform elaborate motions with low hand tremor and high precision compared to the non-dominant hand. We are not arguing that the differences between the left and right hand exactly correspond to the differences between the skills level of novices and experts, we are trying to create two conditions that are different in the performance level to test our approach on.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.070
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 science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0760.006

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 head0.020
GPT teacher head0.269
Teacher spread0.249 · 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