{"id":"W1986566048","doi":"10.1167/10.11.17","title":"Obstacle avoidance during online corrections","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Obstacle; Workspace; Obstacle avoidance; Computer vision; Computer science; Jump; Artificial intelligence; Collision avoidance; Object (grammar); Encoding (memory); Psychology; Communication; Collision; Robot; Computer security; Geography; Mobile robot","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":[0.000182866,0.0002116035,0.0002589646,0.0001388452,0.0001272598,0.0003535926,0.0002188895,0.0002382489,0.001328601],"category_scores_gemma":[0.003568503,0.0001255118,0.0001281836,0.0000753777,0.0002371403,0.0003301178,0.0004951311,0.0003698369,0.0001574501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001206181,"about_ca_system_score_gemma":0.0002568837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009487994,"about_ca_topic_score_gemma":0.0008441432,"domain_scores_codex":[0.9997204,0.0000494619,0.00001569555,0.00005345526,0.0001147885,0.00004614891],"domain_scores_gemma":[0.9988911,0.0005043808,0.0002195889,0.0001683143,0.0001491903,0.00006748406],"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":[0.001169437,0.00008981064,0.01114409,0.0001327865,0.00003064028,0.000227568,0.0005971125,0.001393228,0.9146698,0.0006701653,0.0002259556,0.06964947],"study_design_scores_gemma":[0.0001899084,0.002622628,0.5582088,0.0000806398,0.0001360321,0.001778504,0.0006912763,0.03097684,0.3948497,0.00526176,0.005114877,0.00008895361],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919047,0.0001188357,0.006237241,0.00002035711,0.00001074822,0.00001310925,0.00002394145,0.00008925467,0.001581739],"genre_scores_gemma":[0.9976079,0.00004128639,0.001588078,0.00001398247,0.000003039132,0.00001183781,0.00003854875,0.00002435221,0.0006708886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001328601,"threshold_uncertainty_score":0.00444454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01813803807591603,"score_gpt":0.2875471384150537,"score_spread":0.2694091003391377,"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."}}