{"id":"W2084346734","doi":"10.1167/7.5.4","title":"Computations for geometrically accurate visually guided reaching in 3-D space","year":2007,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Canadian Institutes of Health Research","funders":"","keywords":"Gaze; Computer vision; Computer science; Rotation (mathematics); Eye movement; Artificial intelligence; Head (geology); Visual space; Saccade; Transformation (genetics); Eye tracking; Computation; Algorithm; Psychology; Neuroscience; Perception","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001494029,0.00006679882,0.0001508561,0.0005380446,0.00007414727,0.00005760933,0.0001360301,0.00003832136,0.000005013694],"category_scores_gemma":[0.002944832,0.00005322606,0.0000830674,0.0004707841,0.00001313345,0.0003507842,0.00001636041,0.0001549546,0.00000346531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000690296,"about_ca_system_score_gemma":0.00004476632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005853372,"about_ca_topic_score_gemma":0.000005900015,"domain_scores_codex":[0.9988191,0.00006362063,0.0005371487,0.0001113344,0.0003055787,0.0001631962],"domain_scores_gemma":[0.9981841,0.001110985,0.0004210416,0.00005818781,0.0001486776,0.00007699944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000325831,0.0002012486,0.0002338935,0.0000124436,0.00000315172,0.00006332817,0.0004684797,0.00700898,0.9374121,0.003492922,0.0003019352,0.05047573],"study_design_scores_gemma":[0.01297002,0.004054664,0.6451079,0.0005957104,0.00005684792,0.0004145443,0.0004565096,0.2567699,0.03813786,0.01991739,0.02091673,0.0006018917],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6281207,0.00003400132,0.3699468,0.001210422,0.0002525066,0.0001383616,0.000001338292,0.000007711937,0.0002882213],"genre_scores_gemma":[0.9899533,0.00001894416,0.009490118,0.0003389922,0.0001337677,6.105005e-7,4.476161e-7,0.000007799474,0.00005599923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8992742,"threshold_uncertainty_score":0.3525451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06518901778499131,"score_gpt":0.3801205501212688,"score_spread":0.3149315323362775,"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."}}