{"id":"W4312131410","doi":"10.1101/2022.12.24.521682","title":"Reach corrections toward moving objects are faster than reach corrections toward jumping targets","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada First Research Excellence Fund","keywords":"Jumping; GRASP; Context (archaeology); Computer science; Object (grammar); Position (finance); Computer vision; Projection (relational algebra); Human–computer interaction; Artificial intelligence; Virtual image; Psychology; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004759391,0.0003334361,0.0003270192,0.0003452282,0.00009756216,0.0003335391,0.000194783,0.0004675078,0.005421629],"category_scores_gemma":[0.007154679,0.0002397339,0.0001601608,0.0001732736,0.0003009163,0.0004859264,0.0003505406,0.000708984,0.0004847925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001356778,"about_ca_system_score_gemma":0.0001916724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008010833,"about_ca_topic_score_gemma":0.0005927585,"domain_scores_codex":[0.9995617,0.00006441087,0.00004937384,0.000134233,0.0001527618,0.00003755133],"domain_scores_gemma":[0.9961733,0.00147038,0.001467275,0.0002321112,0.0004085915,0.000248271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001645092,0.0001975471,0.01200964,0.0002650698,0.0000809664,0.0001398783,0.0002704332,0.001328142,0.95096,0.0003780509,0.0003366157,0.03238868],"study_design_scores_gemma":[0.0001300646,0.002017609,0.7982326,0.00004898118,0.0001169718,0.0009118975,0.0002675801,0.01185646,0.1830519,0.001395206,0.001918068,0.00005270684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907618,0.0003750849,0.006521372,0.00008655283,0.00003553819,0.00004509919,0.0001400777,0.0002569828,0.001777577],"genre_scores_gemma":[0.9967878,0.00009299583,0.001710779,0.0000401056,0.000007915704,0.00003849128,0.0000999308,0.00007338623,0.001148545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005421629,"threshold_uncertainty_score":0.01813722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03733281644213636,"score_gpt":0.241158616699257,"score_spread":0.2038258002571207,"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."}}