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Record W2291006022 · doi:10.1123/jab.2014-0101

Segment Kinematics Differ Between Jump and Drop Landings Regardless of Practice

2015· article· en· W2291006022 on OpenAlexafffund
Loren Z.F. Chiu, Amy N. Moolyk

Bibliographic record

VenueJournal of Applied Biomechanics · 2015
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsKinematicsJumpPelvisDrop (telecommunication)Physical medicine and rehabilitationMathematicsBiomechanicsKnee flexionFoot dropOrthodonticsMechanicsGeologyMedicineAnatomyComputer sciencePhysicsClassical mechanics

Abstract

fetched live from OpenAlex

Joint kinematics differ between jump and drop landings and there is evidence that segment kinematics may also be different. The purpose of this research was to compare lower extremity segment kinematics for jump and drop landings, and to examine if multiple days of practice would influence these kinematics. Men (n = 9) and women (n = 15) performed 4 sessions of jump and drop landings (40 cm and 60 cm) in a motion-capture laboratory. Segment kinematics at initial contact, foot flat, and peak knee flexion were compared between landing types and across visits. At initial contact, foot plantar flexion was greater in jump versus drop landings (P < .05). At initial contact and foot flat, forward leg inclination and pelvis flexion were greater in jump landing (P < .05), while thigh flexion was greater in drop landings (P > .05). The differences in leg and thigh angles at initial contact and foot flat altered lower extremity posture. These results are in contrast to a previous study; this suggests that drop landing can be modified to have the same mechanics as jump landing. As practice did not influence drop landing mechanics (P > .05), specific control strategies and instructions need to be identified.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.036
GPT teacher head0.299
Teacher spread0.263 · 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

Citations9
Published2015
Admission routes2
Has abstractyes

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