{"id":"W2060634510","doi":"10.1080/01431160802392646","title":"Finite Gamma mixture modelling using minimum message length inference: application to SAR image analysis","year":2009,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Université de Sherbrooke","funders":"","keywords":"Computer science; Inference; Set (abstract data type); Image (mathematics); Segmentation; Data set; Synthetic aperture radar; Finite set; Artificial intelligence; Image segmentation; Unsupervised learning; Synthetic data; Pattern recognition (psychology); Algorithm; Data mining; Mathematics","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":[0.004417632,0.0006977727,0.001167503,0.001607398,0.0006523504,0.001164907,0.001279288,0.001486387,0.0009246336],"category_scores_gemma":[0.02031918,0.0006117856,0.0008997854,0.001407226,0.001335697,0.001566934,0.00168121,0.001743546,0.0003993547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001005368,"about_ca_system_score_gemma":0.0009215648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002834189,"about_ca_topic_score_gemma":0.002611076,"domain_scores_codex":[0.998626,0.0008261732,0.00005300077,0.0001704219,0.0002782726,0.00004617209],"domain_scores_gemma":[0.9891228,0.009222596,0.0005500655,0.0004541974,0.0005289383,0.0001214423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001178032,0.00006749215,0.001811028,0.0001256461,0.000110769,0.0001322595,0.0003119373,0.8158161,0.004127478,0.04059937,0.001010845,0.1357693],"study_design_scores_gemma":[0.000005752896,0.000009960865,0.0001423704,0.000007165147,0.000004923472,0.00002489571,0.000006873314,0.9754034,0.0007036596,0.02334276,0.0003352984,0.00001283421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00412646,0.0001329621,0.9952319,0.0001126997,0.000006083558,0.00001329072,0.00001334266,0.0001480267,0.0002152823],"genre_scores_gemma":[0.2001668,0.0004346718,0.7975242,0.000129486,0.00009999158,0.0001627461,0.000190188,0.0001768209,0.001115112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004417632,"threshold_uncertainty_score":0.02336293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02196281022088925,"score_gpt":0.3197440377333011,"score_spread":0.2977812275124119,"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."}}