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Record W2068588248 · doi:10.1080/00222895.2014.931272

Simulated Visual Field Loss Does Not Alter Turning Coordination in Healthy Young Adults

2014· article· en· W2068588248 on OpenAlexaff
Nicholas G. Murray, Marlina Ponce de Leon, V. N. Pradeep Ambati, Fabricio Saucedo, E. C. Kennedy, Rebecca J. Reed-Jones

Bibliographic record

VenueJournal of Motor Behavior · 2014
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Prince Edward Island
FundersUniversity of Texas at El Paso
KeywordsVisual fieldFlexibility (engineering)Physical medicine and rehabilitationPsychologyVisual impairmentYoung adultMotor controlAudiologyMotor coordinationPeripheralDevelopmental psychologyNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Turning, while walking, is an important component of adaptive locomotion. Current hypotheses regarding the motor control of body segment coordination during turning suggest heavy influence of visual information. The authors aimed to examine whether visual field impairment (central loss or peripheral loss) affects body segment coordination during walking turns in healthy young adults. No significant differences in the onset time of segments or intersegment coordination were observed because of visual field occlusion. These results suggest that healthy young adults can use visual information obtained from central and peripheral visual fields interchangeably, pointing to flexibility of visuomotor control in healthy young adults. Further study in populations with chronic visual impairment and those with turning difficulties are warranted.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.376
Teacher spread0.359 · 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 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

Citations7
Published2014
Admission routes1
Has abstractyes

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