{"id":"W2789584240","doi":"10.1371/journal.pone.0192476","title":"Context-dependent concurrent adaptation to static and moving targets","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Tracking (education); Adaptation (eye); Context (archaeology); Movement (music); Computer science; Artificial intelligence; Physical medicine and rehabilitation; Computer vision; Psychology; Neuroscience; Physics; Biology; Medicine","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":[0.0001829119,0.000264691,0.000380589,0.0001173098,0.000109195,0.0001860682,0.0002957388,0.0002336583,0.0009291724],"category_scores_gemma":[0.0008220107,0.0001755277,0.0002314195,0.00006444904,0.0003670711,0.0002617452,0.0006041877,0.0006361609,0.0001041483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001807364,"about_ca_system_score_gemma":0.0001884822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009025434,"about_ca_topic_score_gemma":0.001564384,"domain_scores_codex":[0.9997727,0.00001885245,0.00001816347,0.00007742873,0.00005579494,0.00005706918],"domain_scores_gemma":[0.9997165,0.00005932586,0.00006198872,0.00007771429,0.00003057126,0.00005382531],"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":[0.0001101323,0.00002352662,0.0004092219,0.00001171655,0.000005056952,0.00002661085,0.00001769411,0.0001175659,0.9970836,0.00003559902,0.000005817763,0.002153241],"study_design_scores_gemma":[0.00003468161,0.001932082,0.2024455,0.00002236584,0.00007438448,0.0006682586,0.0001300783,0.006480988,0.7866247,0.0004811299,0.00107015,0.00003566723],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967579,0.0001070268,0.002306009,0.00001870739,0.00001424989,0.00001437828,0.00001890986,0.00002745437,0.0007354065],"genre_scores_gemma":[0.9982994,0.00007221665,0.001034808,0.00003811707,0.000003676475,0.00002077163,0.00003860815,0.00001314278,0.0004792522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009291724,"threshold_uncertainty_score":0.003108382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08935330270937504,"score_gpt":0.2592497425672309,"score_spread":0.1698964398578559,"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."}}