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Interlimb co‐ordination in human infant stepping

2001· article· en· W2170172578 on OpenAlexaff
Marco Y.C. Pang, Jaynie F. Yang

Bibliographic record

VenueThe Journal of Physiology · 2001
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSwingPhysical medicine and rehabilitationTreadmillBalance (ability)MedicinePsychologyPhysical therapyPhysics

Abstract

fetched live from OpenAlex

1. We held infants (aged 4-12 months) over a treadmill to study how they co-ordinated the two limbs during stepping. We disturbed one limb during the stance or swing phase and recorded the responses (muscle activity and movement) from both lower limbs. Manual disturbances were applied during the stance phase by sliding the foot backward, forcing the limb into the swing phase. Disturbances were also applied in the swing phase by manually extending the hip, interfering with the forward motion of the limb. Additional disturbances were applied to see if both limbs could perform the stance and swing phase synchronously. 2. When the limb was forced to initiate the swing phase on one side, the contralateral limb either prolonged its contact with the ground or quickly established ground contact. When the forward motion of the limb was interrupted in the swing phase, the swing phase was prolonged on the disturbed side and the stance phase prolonged on the contralateral side. In most cases, one leg maintained ground contact. Moreover, it was easy to elicit bilateral, simultaneous stance phase, whereas it was difficult to elicit simultaneous swing phase. In cases where swing phase in the two limbs was initiated close in time, rhythmic alternate stepping was immediately restored in the following step. 3. We conclude that human infants can generate co-ordinated motor responses bilaterally in response to unilateral perturbations, well before the onset of independent walking.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.236

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 science0.0000.000
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 head0.022
GPT teacher head0.317
Teacher spread0.296 · 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 designBench or experimental
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

Citations76
Published2001
Admission routes1
Has abstractyes

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