{"id":"W2393778159","doi":"","title":"A moving target tracking algorithm based on adaptive multiple cues fusion","year":2010,"lang":"en","type":"article","venue":"Journal of Optoelectronics·laser","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Tracking (education); Artificial intelligence; Weighting; Computer science; Computer vision; Fusion; Algorithm; Variance (accounting); Enhanced Data Rates for GSM Evolution; Kernel (algebra); Pattern recognition (psychology); 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001148715,0.000290353,0.0004014916,0.0004375637,0.0001430289,0.00007333631,0.0003035268,0.0002370554,0.0001952784],"category_scores_gemma":[0.0006663288,0.0002599183,0.000257316,0.0002825962,0.00004065148,0.0004230773,0.00002461742,0.001910303,0.00001675977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002523788,"about_ca_system_score_gemma":0.0001368165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003026661,"about_ca_topic_score_gemma":0.000007566974,"domain_scores_codex":[0.9981273,0.0001351252,0.0005611348,0.0001843011,0.0004429991,0.0005490718],"domain_scores_gemma":[0.9984452,0.0006331866,0.0002583236,0.0002524554,0.0002743321,0.0001364607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002945287,0.0001082036,0.0004367387,0.00002875205,0.0001330954,0.00009431541,0.0002499669,0.6223216,0.3054477,0.00004247304,0.0008586108,0.06998399],"study_design_scores_gemma":[0.0007463659,0.0004806551,0.0004950576,0.00004157309,0.00002342814,0.00007282324,0.00007388611,0.4732279,0.5213788,0.0006604435,0.002565879,0.0002332427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5749376,0.0002430078,0.4214781,0.0000895378,0.00223333,0.0001672936,0.00001299194,0.0002203518,0.0006176947],"genre_scores_gemma":[0.6329771,0.00003545506,0.366365,0.00006941812,0.0004624825,0.00000546758,0.00000226749,0.00005746388,0.00002534719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2159311,"threshold_uncertainty_score":0.9999853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01583656893547797,"score_gpt":0.2499487454741837,"score_spread":0.2341121765387058,"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."}}