{"id":"W3032997645","doi":"10.3390/rs12111746","title":"A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Synthetic aperture radar; Artificial intelligence; Thresholding; Pattern recognition (psychology); Benchmark (surveying); Change detection; Image (mathematics); Remote sensing; Computer vision; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00004993466,0.000251969,0.0002471756,0.00005386375,0.0001015374,0.00005322481,0.0000677006,0.0001531484,0.000001841201],"category_scores_gemma":[0.0001244452,0.0002804165,0.0001119981,0.00008375386,0.00005181985,0.0002381592,0.00002238198,0.0002163318,0.00001997391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002373436,"about_ca_system_score_gemma":0.00000924273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005651649,"about_ca_topic_score_gemma":0.00001612613,"domain_scores_codex":[0.9989568,0.00002241145,0.0002134728,0.000374783,0.0001642541,0.0002683038],"domain_scores_gemma":[0.9994155,0.000141529,0.00007520115,0.0002235749,0.00002291344,0.0001212865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003919427,0.000004790119,3.842513e-7,0.00001918099,0.00002796973,0.000003277092,0.0009060887,0.004120013,0.7261449,1.406942e-7,0.00002029662,0.2687138],"study_design_scores_gemma":[0.000498822,0.00001727584,0.00008960992,0.00005145317,0.00004426778,0.00001259206,0.0001967201,0.8436543,0.1546237,0.0000175554,0.0005475347,0.0002461858],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1055867,0.0001494151,0.8922644,0.0006466609,0.0001915556,0.0005207814,0.0001510697,0.0003735225,0.0001159317],"genre_scores_gemma":[0.8668921,0.00002652006,0.1322967,0.0002567799,0.000326725,1.270415e-7,0.00007993908,0.00009827326,0.00002287091],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8395343,"threshold_uncertainty_score":0.9999648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04217188850840395,"score_gpt":0.2225602195000561,"score_spread":0.1803883309916522,"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."}}