{"id":"W2104678017","doi":"10.1109/iros.2007.4399527","title":"Where is your dive buddy: tracking humans underwater using spatio-temporal features","year":2007,"lang":"en","type":"article","venue":"","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Underwater; Tracking (education); Robot; Motion (physics); Mobile robot; Energy (signal processing); Geology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001865428,0.0003033896,0.0002921695,0.0008329307,0.0002879724,0.0003298564,0.0004649043,0.0006222925,0.0006179405],"category_scores_gemma":[0.0005228068,0.0001796568,0.000204239,0.0005431491,0.0002482126,0.0004539912,0.0003895329,0.0002543176,0.0003776467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001628174,"about_ca_system_score_gemma":0.0002476084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003049177,"about_ca_topic_score_gemma":0.005923944,"domain_scores_codex":[0.9999211,0.000009173826,0.000004064203,0.00002775256,0.00002465319,0.00001320813],"domain_scores_gemma":[0.9998524,0.00003873686,0.00002903765,0.00001882688,0.00004313619,0.00001776994],"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":[0.0002343644,0.0001566756,0.01579447,0.0001007981,0.00007492472,0.0002928354,0.0003310492,0.01880997,0.05825337,0.001901739,0.004700812,0.8993489],"study_design_scores_gemma":[0.00003304728,0.0002499801,0.03598082,0.00005330664,0.00009091014,0.00138202,0.000402129,0.9011301,0.04579037,0.006236554,0.008599093,0.0000517058],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1859753,0.0004864497,0.8092638,0.0003146165,0.00007975793,0.00009756166,0.0003520683,0.001179296,0.002251229],"genre_scores_gemma":[0.5098706,0.0002774203,0.4857503,0.0001131171,0.00004962786,0.0001089979,0.0004416816,0.00004574506,0.003342568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003049177,"threshold_uncertainty_score":0.006062806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03499847224880494,"score_gpt":0.272307381163772,"score_spread":0.2373089089149671,"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."}}