{"id":"W1963947172","doi":"10.1117/12.666039","title":"An EM-IMM based abrupt change detector for landmine detection","year":2006,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Detector; Ground-penetrating radar; Change detection; Computer science; Kalman filter; Maximization; Artificial intelligence; A priori and a posteriori; Expectation–maximization algorithm; Receiver operating characteristic; Radar; Computer vision; Filter (signal processing); Pattern recognition (psychology); Maximum likelihood; Machine learning; Mathematics; Telecommunications; Statistics","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":[0.001331267,0.0004978765,0.0007108648,0.000565071,0.0002699335,0.0005789514,0.00112562,0.00125456,0.000978481],"category_scores_gemma":[0.00315912,0.0003027366,0.0005503778,0.0004356602,0.0005585689,0.00126086,0.000834157,0.001101347,0.0005500784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003500056,"about_ca_system_score_gemma":0.0004592553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005324399,"about_ca_topic_score_gemma":0.0007240591,"domain_scores_codex":[0.9995116,0.00011053,0.00002612095,0.0001120531,0.0001977331,0.00004198249],"domain_scores_gemma":[0.9989809,0.0004569558,0.0001095931,0.000118007,0.0002865357,0.00004803926],"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":[0.0004102038,0.0002271849,0.005395948,0.0002478818,0.0002106817,0.0003267113,0.0001361667,0.3533443,0.04623047,0.02246823,0.003674336,0.5673279],"study_design_scores_gemma":[0.000009375077,0.0000684569,0.0004738242,0.000004892327,0.00001195193,0.0001337642,0.000008534038,0.9851857,0.01006279,0.002201955,0.001822271,0.00001644165],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004168093,0.00008547569,0.9949958,0.00005202837,0.00002620905,0.00001250431,0.0000163743,0.0003221339,0.000321334],"genre_scores_gemma":[0.3404863,0.0002313986,0.6557906,0.0002378014,0.00007233142,0.00007489862,0.0001814585,0.00007241114,0.002852888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001331267,"threshold_uncertainty_score":0.007040501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01447784683039033,"score_gpt":0.2421731386277243,"score_spread":0.227695291797334,"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."}}