{"id":"W1509362770","doi":"10.1007/978-3-642-15470-6_35","title":"Robust and Efficient Change Detection Algorithm","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Change detection; Computer science; Pixel; Fault detection and isolation; Artificial intelligence; Image (mathematics); Algorithm; Noise (video); Vulnerability (computing); Range (aeronautics); Ranging; Pattern recognition (psychology); Data mining; Engineering; Computer security; Telecommunications","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":[0.0005989972,0.0009151511,0.001504512,0.0017459,0.0005138112,0.00104766,0.001650317,0.001209515,0.004531651],"category_scores_gemma":[0.001255375,0.0005759488,0.001035505,0.001609557,0.0004093163,0.001235157,0.001070463,0.0009763672,0.003460359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003990111,"about_ca_system_score_gemma":0.0007975002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001674836,"about_ca_topic_score_gemma":0.002608674,"domain_scores_codex":[0.9992077,0.00006460209,0.0000417003,0.0002249199,0.0003950149,0.0000661013],"domain_scores_gemma":[0.9993783,0.0001279223,0.00005011874,0.0001648177,0.0002567978,0.00002193095],"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":[0.0001469925,0.00008851619,0.000475924,0.00009863766,0.00007130221,0.00007874655,0.00002811645,0.01923499,0.05907371,0.004429885,0.007963073,0.90831],"study_design_scores_gemma":[0.00003812687,0.000137653,0.002674791,0.00001800797,0.0001126368,0.0006782401,0.0000259158,0.8905132,0.07543327,0.005076389,0.02523008,0.0000616196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005674442,0.0004619664,0.9902045,0.00008444647,0.0001152168,0.00005533937,0.00011176,0.001720628,0.001571754],"genre_scores_gemma":[0.06463055,0.0004015763,0.9247334,0.0001222503,0.0001090465,0.00009932299,0.0006938179,0.0002600936,0.008949942],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004531651,"threshold_uncertainty_score":0.0151599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02505207593220522,"score_gpt":0.2119525687382111,"score_spread":0.1869004928060058,"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."}}