{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005574522,0.000548584,0.0008287355,0.00083693,0.0004507201,0.0005791164,0.001108469,0.001007426,0.000901014],"category_scores_gemma":[0.0009880423,0.0003371991,0.0006189936,0.0009738698,0.000337696,0.001154691,0.0009240391,0.0009214683,0.0004550963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004627659,"about_ca_system_score_gemma":0.0006051589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002559919,"about_ca_topic_score_gemma":0.001695435,"domain_scores_codex":[0.9995498,0.00004617242,0.00002182517,0.0001220823,0.0002120022,0.00004816361],"domain_scores_gemma":[0.9997141,0.00005705059,0.00003669271,0.00002711124,0.0001441856,0.00002083995],"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":[0.0002348818,0.00007845707,0.0008555146,0.000130616,0.000101349,0.0001432315,0.0001527963,0.09248769,0.1329449,0.01198131,0.002332368,0.7585569],"study_design_scores_gemma":[0.00002512798,0.0001090561,0.0005421609,0.00001058076,0.00003692831,0.0002465596,0.00001511033,0.9677973,0.02626153,0.001650725,0.003265447,0.00003954589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00470144,0.0001909934,0.9941415,0.00003907866,0.00004644721,0.00001950904,0.00001051707,0.0003452369,0.0005051994],"genre_scores_gemma":[0.2389807,0.0004081408,0.7575096,0.0001021832,0.00007130266,0.0001090102,0.0001139504,0.00006581357,0.002639222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002559919,"threshold_uncertainty_score":0.005089998,"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."}}