Changes in infant segment inertias during the first three months of independent walking
Bibliographic record
Abstract
BACKGROUND: During infancy, rapid changes in physical growth affect the size and shape of the body segments. To understand the effects of growth on movement, it is first necessary to quantify rates of development during the acquisition of important motor milestones. The goal of this longitudinal study was to quantify the physical growth of infant body segments during the initial stages of independent walking. METHODS: Ten infants (N = 10) aged between 28 and 55 weeks at the beginning of the study were tested biweekly (every two weeks) for three months. A 13-segment mathematical model of the human body was used to estimate the inertial parameters of the infant body segments at each session. An analysis of variance was used to test for significant differences in segment masses between biweekly measures. Polynomial contrasts were used to test for linear trends in the growth data. RESULTS: Significant differences between biweekly measures of segment mass were found only for the head/neck (F(5,45) = 3.42, p < 0.05), upper trunk (F(5,45) = 4.04, p < 0.01), and lower trunk (F(5,45) = 3.49, p < 0.01). The lower trunk demonstrated a linear increase in mass (F(1,9) = 4.56, p < 0.05). However, the upper trunk demonstrated a quadratic trend in growth (F(1,9) = 9.13, p < 0.01), while the head/neck segment showed a cubic trend in growth (F(1,9) = 3.80, p < 0.05). Significant differences in axial segment masses were also found between subjects (F(9,45) = 5.92, p < 0.001). CONCLUSION: Given that postural control proceeds in a cephalocaudal manner, the lower trunk segment would be brought under control last, in terms of the axial segments. Increases in the mass of this segment could constrain the system, thereby acting as a control parameter for the onset and development of motor patterns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".