{"id":"W2728039030","doi":"","title":"RESULTS AND ANALYSIS OF COHERENT CHANGE DETECTION EXPERIMENTS USING REPEAT-PASS SYNTHETIC APERTURE SONAR IMAGES","year":2013,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Synthetic aperture sonar; Sonar; Change detection; Computer science; Synthetic aperture radar; Artificial intelligence; Sonar signal processing; Remote sensing; Computer vision; Signal processing; Geology; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002601674,0.0002772186,0.0004639067,0.0005727107,0.0002569168,0.0003440798,0.0006249013,0.0002566044,0.0003941237],"category_scores_gemma":[0.0005549112,0.0002570907,0.0001655122,0.0006520629,0.0002882493,0.0001709679,0.0004241888,0.0004355893,0.00001581831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004132175,"about_ca_system_score_gemma":0.00008339473,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03348628,"about_ca_topic_score_gemma":0.008564958,"domain_scores_codex":[0.9950042,0.002835094,0.000520361,0.0007641493,0.0005266428,0.0003494953],"domain_scores_gemma":[0.9958492,0.001229777,0.0004567077,0.001222011,0.001043305,0.0001990068],"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":[0.0003144812,0.001238078,0.1177107,0.001446626,0.004177666,0.00004151328,0.05173632,0.01407526,0.1037075,0.0001986002,0.0002539393,0.7050993],"study_design_scores_gemma":[0.0003293749,0.00000159497,0.06967758,0.0006161417,0.0003659982,0.000006608962,0.0002108569,0.885702,0.04191448,0.0003442291,0.0004636019,0.0003675919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8714617,0.002785386,0.1147901,0.001980554,0.0001818543,0.001107804,0.001219204,0.0001199816,0.006353481],"genre_scores_gemma":[0.9769714,0.0009617184,0.02038527,0.00002573824,0.00001594229,0.00001768225,0.0009699175,0.0000134629,0.0006388231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8716267,"threshold_uncertainty_score":0.9999881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0427393439852012,"score_gpt":0.2624519392232307,"score_spread":0.2197125952380295,"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."}}