{"id":"W1976774657","doi":"10.1117/12.541894","title":"&lt;title&gt;A randomized heuristic approach for multidimensional association in target tracking&lt;/title&gt;","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Heuristic; Computational complexity theory; Computer science; Relaxation (psychology); Mathematical optimization; Tracking (education); Tree (set theory); Algorithm; Artificial intelligence; Mathematics","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":[0.0008539531,0.0001589659,0.0002960952,0.00009176705,0.0000410852,0.00008773564,0.0006115861,0.0001020908,0.00001234288],"category_scores_gemma":[0.0006062754,0.0001334855,0.0003382249,0.000218689,0.00005611488,0.0004187394,0.0001226655,0.0001373065,0.000007583856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001608534,"about_ca_system_score_gemma":0.00002539694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002037738,"about_ca_topic_score_gemma":4.034468e-8,"domain_scores_codex":[0.9986479,2.738006e-8,0.0003833863,0.0002724917,0.0004552957,0.0002409273],"domain_scores_gemma":[0.9990557,0.0001285574,0.0002208689,0.00004549808,0.0005029298,0.00004644058],"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":[0.00009686738,0.0001051862,0.00001934718,0.0001378279,0.0001491137,7.249351e-8,0.00006678092,0.0002492452,0.01072733,0.9786364,0.009347954,0.0004638256],"study_design_scores_gemma":[0.02713353,0.0001978921,0.0003878355,0.0003152167,0.0001842498,0.000007874543,0.0001335954,0.8750769,0.01418751,0.02541867,0.05617408,0.0007826524],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8132283,0.0005539477,0.1069296,0.006809305,0.001938627,0.00335451,0.0001571126,0.0004131699,0.06661545],"genre_scores_gemma":[0.04926355,0.00006432612,0.9483565,0.0001172051,0.0003833905,0.0001921089,0.00003687559,0.00003711247,0.001548943],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9532177,"threshold_uncertainty_score":0.5443379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01009906195535383,"score_gpt":0.2192068384838709,"score_spread":0.2091077765285171,"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."}}