{"id":"W1606092414","doi":"","title":"Analyzing the kinematics of bivariate pointing","year":2008,"lang":"en","type":"article","venue":"","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Kinematics; Jerk; Constraint (computer-aided design); Acceleration; Computer science; Motion (physics); Artificial intelligence; Bivariate analysis; Computer vision; Movement (music); Work (physics); Fitts's law; Control theory (sociology); Mathematics; Machine learning; Engineering; Geometry; Acoustics; Physics; Control (management)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000638038,0.0003960865,0.0004303839,0.001218478,0.0001629737,0.0005467307,0.0002147689,0.0002308547,0.001986774],"category_scores_gemma":[0.007986196,0.0002407085,0.0003107097,0.0009305692,0.0002834277,0.0005146521,0.0004229631,0.0002786477,0.0004300282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001858587,"about_ca_system_score_gemma":0.000359564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002618833,"about_ca_topic_score_gemma":0.002218751,"domain_scores_codex":[0.9996371,0.00008275545,0.00003528008,0.0001017323,0.0001023141,0.00004077367],"domain_scores_gemma":[0.9980507,0.0009495853,0.0003978599,0.0001892811,0.0003333104,0.00007931083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001053705,0.0001446157,0.162543,0.0004125744,0.0001931466,0.000591471,0.0008650723,0.1657741,0.2115522,0.008700284,0.0008266449,0.4473432],"study_design_scores_gemma":[0.00004104138,0.0004435368,0.4077096,0.00006539626,0.0000642431,0.0008007484,0.0003755927,0.5551068,0.02563922,0.007917784,0.001733649,0.0001023973],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6972703,0.0004312465,0.2988795,0.00007866137,0.00002466394,0.00004677118,0.0002720399,0.0006114146,0.002385383],"genre_scores_gemma":[0.9733146,0.0002325766,0.02542497,0.000007625413,0.000009422945,0.00002089525,0.0002287659,0.00004303445,0.0007182495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002618833,"threshold_uncertainty_score":0.006646395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07527032024785327,"score_gpt":0.2967559289366679,"score_spread":0.2214856086888146,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}