{"id":"W2611792579","doi":"10.1145/3025453.3025951","title":"Modeling User Performance on Curved Constrained Paths","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Tree traversal; Constraint (computer-aided design); Path (computing); Computer science; Curvature; Work (physics); Trajectory; Algorithm; Motion planning; Mathematical optimization; Control theory (sociology); Mathematics; Geometry; Artificial intelligence; Robot; Engineering; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002132772,0.0003011639,0.0002900609,0.0001166474,0.0002453619,0.0004223062,0.001952528,0.000182495,0.00007127117],"category_scores_gemma":[0.00005703185,0.0002559535,0.0001833187,0.00002805048,0.00004632399,0.0005110464,0.001290344,0.0006690327,0.0004026042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006429412,"about_ca_system_score_gemma":0.0001862476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009713972,"about_ca_topic_score_gemma":0.000003971219,"domain_scores_codex":[0.998419,0.00004441879,0.000236554,0.0006976051,0.0002743891,0.0003279605],"domain_scores_gemma":[0.9979785,0.00004129896,0.0001599041,0.001470763,0.0002717636,0.00007775347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004347728,0.001338591,0.002716454,0.0009799142,0.001292003,0.0003450154,0.008171472,0.3638861,0.0260287,0.5032044,0.02266833,0.06893417],"study_design_scores_gemma":[0.0002230935,0.00007524278,0.0004936153,0.0002560338,0.000007683872,0.000004910803,0.000027484,0.9899677,0.007301128,0.0007944153,0.0004669439,0.0003817514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1683021,0.00002387051,0.701724,0.0005894671,0.002128874,0.0002800949,0.00001298766,0.00006214945,0.1268764],"genre_scores_gemma":[0.9924927,0.00004962664,0.004122207,0.0007476835,0.0001359215,0.00002553083,0.00001875309,0.00001358692,0.002393947],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8241906,"threshold_uncertainty_score":0.9999893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04547038131072585,"score_gpt":0.2929342057076068,"score_spread":0.247463824396881,"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."}}