{"id":"W1996241045","doi":"10.1109/maes.2003.1224967","title":"Voting fusion adaptation for landmine detection","year":2003,"lang":"en","type":"article","venue":"IEEE Aerospace and Electronic Systems Magazine","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Dynamics (Canada)","funders":"","keywords":"Voting; Sensor fusion; False alarm; Computer science; Fusion; Constant false alarm rate; Heuristic; Scheme (mathematics); Real-time computing; Artificial intelligence; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001007734,0.0004193092,0.0006822265,0.0003995895,0.0003769827,0.0004729322,0.000862464,0.0005660292,0.001490142],"category_scores_gemma":[0.003007221,0.0001931544,0.0003899288,0.0006441063,0.0003890305,0.0008129432,0.0007672284,0.000585814,0.0004951686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003464464,"about_ca_system_score_gemma":0.0002995309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001657646,"about_ca_topic_score_gemma":0.001868367,"domain_scores_codex":[0.9990963,0.0002275497,0.00004278167,0.0001949025,0.0003419958,0.00009661155],"domain_scores_gemma":[0.9991863,0.0002746278,0.00006322026,0.0001751849,0.0002753747,0.00002519524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005360048,0.00009498135,0.002430683,0.00009407212,0.000105063,0.00019044,0.0001537962,0.2016326,0.09750434,0.009532877,0.002457362,0.6852678],"study_design_scores_gemma":[0.00001705727,0.000115919,0.001071854,0.000005318661,0.00002589611,0.0001545874,0.00002429446,0.954854,0.03596624,0.004164075,0.003576922,0.00002380321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03934341,0.000192393,0.9571722,0.00006888789,0.00008089829,0.00005325737,0.00003148658,0.001041954,0.002015546],"genre_scores_gemma":[0.826136,0.0001272003,0.1708417,0.00009272501,0.00005292374,0.00006918737,0.000123308,0.00007662413,0.002480364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001657646,"threshold_uncertainty_score":0.00532949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01234172138750951,"score_gpt":0.227546653162086,"score_spread":0.2152049317745765,"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."}}