{"id":"W2052694701","doi":"10.1063/1.3573633","title":"Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields","year":2011,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Space Agency; Università degli Studi di Genova; Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Synthetic aperture radar; Markov random field; Pattern recognition (psychology); Artificial intelligence; Contextual image classification; Computer science; Copula (linguistics); Radar imaging; Probability density function; Bayesian probability; Random field; Markov process; Markov chain; Mathematics; Radar; Machine learning; Image segmentation; Image (mathematics); Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001050684,0.0002328948,0.000255718,0.00008594735,0.0001506051,0.0002581431,0.0006566297,0.000188133,0.00006069486],"category_scores_gemma":[0.0003670674,0.0001982191,0.00005201023,0.0001706154,0.000141959,0.0007470329,0.0001876344,0.0002706137,0.00001316367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001426658,"about_ca_system_score_gemma":0.00005192812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003331585,"about_ca_topic_score_gemma":0.000001233013,"domain_scores_codex":[0.9985615,0.00007339883,0.0002668296,0.0005760057,0.0001863117,0.0003359527],"domain_scores_gemma":[0.9989526,0.0001867689,0.0001537453,0.0002379326,0.0002652024,0.0002038014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003474484,0.0001473959,0.000815398,0.0001185948,0.00004949785,0.000007560255,0.01862182,9.904087e-9,0.05724793,0.7111921,0.009123717,0.2023286],"study_design_scores_gemma":[0.002881198,0.0003826095,0.001812112,0.0001615631,0.00007207815,0.00004880926,0.0004194654,0.1699508,0.02693176,0.7930065,0.003304563,0.001028533],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003129061,0.0002929387,0.9832463,0.0005333489,0.000137471,0.0002718282,0.000005247957,0.0001395759,0.01224421],"genre_scores_gemma":[0.7478489,0.0001547583,0.2506018,0.0007181411,0.00003439103,0.00002754047,0.00000297518,0.00001179857,0.0005997152],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7447198,"threshold_uncertainty_score":0.808314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03013594622518842,"score_gpt":0.2571386688680626,"score_spread":0.2270027226428742,"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."}}