{"id":"W2078018431","doi":"10.1068/p5023","title":"Search of Jumping Items: Visual Marking and Discrete Motion","year":2003,"lang":"en","type":"article","venue":"Perception","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Jumping; Motion (physics); Psychology; Computer science; Artificial intelligence; Computer vision; Cognitive psychology; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008660689,0.0002647041,0.000305586,0.001126396,0.0002545148,0.001832887,0.0004825173,0.0008167844,0.003399236],"category_scores_gemma":[0.01016428,0.0003870484,0.0002602373,0.0009758659,0.001578961,0.003181314,0.0007538835,0.0007991254,0.0002476696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004783221,"about_ca_system_score_gemma":0.0002460784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001378752,"about_ca_topic_score_gemma":0.001338998,"domain_scores_codex":[0.9996831,0.00007814669,0.00002401867,0.00008981788,0.00009833342,0.00002646674],"domain_scores_gemma":[0.9945394,0.00339709,0.001239938,0.0002934039,0.0003203857,0.0002097941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001593483,0.0003602068,0.1001192,0.002972696,0.0004512971,0.001464674,0.0120825,0.004924906,0.07920901,0.1168084,0.01033753,0.6696761],"study_design_scores_gemma":[0.0002969034,0.0008624246,0.6592323,0.0005277457,0.0004261023,0.005903337,0.004009175,0.02198448,0.01537193,0.2562602,0.03488886,0.0002366712],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8532462,0.04439867,0.05796497,0.003979438,0.0001173608,0.00006779834,0.0002957817,0.0001640184,0.03976573],"genre_scores_gemma":[0.9841989,0.00583067,0.008370138,0.000209991,0.00005603133,0.00002510373,0.0001631707,0.00002265214,0.001123269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003399236,"threshold_uncertainty_score":0.01137155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03080505175499932,"score_gpt":0.3323233863280391,"score_spread":0.3015183345730398,"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."}}