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Record W1982765632 · doi:10.1080/00222890109601919

Dynamics of Pushing

2001· article· en· W1982765632 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

VenueJournal of Motor Behavior · 2001
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsCegep de Sainte Foy
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsMagnitude (astronomy)Center of pressure (fluid mechanics)Dynamics (music)Range (aeronautics)Control theory (sociology)Motor controlFoot (prosody)PhysicsComputer scienceMechanicsAcousticsPsychologyControl (management)EngineeringNeuroscienceArtificial intelligence

Abstract

fetched live from OpenAlex

A standing individual can use several strategies for modulating pushing force magnitude. Using a static model, researchers have shown that the efficacy of those strategies varies considerably. In the present article, the authors propose a human motor control dynamic model for analyzing transients that occur when an individual is asked to modulate force magnitude. According to the model, the impedances of both the upper and the lower limbs influence the time course of force variations and foot placement has a profound effect on pushing force dynamics. With a feet-together posture, the center of pressure has a limited range of motion and changes in force may be preceded by initial changes in the opposite direction; that is, to decrease force, an individual must first increase force. When the feet are placed apart, individuals can move the center of pressure over a much larger range, thereby modulating pushing force magnitude, without reversing behavior, over a larger range of force magnitudes. Therefore, the best way to control pushing force at the hand may be by using the foot.

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 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 score0.864
Threshold uncertainty score0.214

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 head0.039
GPT teacher head0.285
Teacher spread0.246 · 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