{"id":"W3196006465","doi":"10.1080/01431161.2021.1939910","title":"Deep support vector machine for PolSAR image classification","year":2021,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Support vector machine; Pattern recognition (psychology); Computer science; Artificial neural network; Confusion matrix; Synthetic aperture radar; Parametric statistics; Contextual image classification; Mathematics; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001590582,0.00009617961,0.0001424302,0.0001114071,0.0000317502,0.00005827064,0.0001438284,0.00005663204,0.0000448815],"category_scores_gemma":[0.0001212454,0.00009309631,0.0001298805,0.00007378764,0.00002122538,0.0001080048,0.00001579923,0.0001347728,0.000006180278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001350848,"about_ca_system_score_gemma":0.00004525491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004693385,"about_ca_topic_score_gemma":0.000006665846,"domain_scores_codex":[0.9992031,0.00001290371,0.000352083,0.00009157212,0.000234548,0.0001058271],"domain_scores_gemma":[0.9989401,0.00009763609,0.0001294061,0.0001245866,0.0006578109,0.00005043083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001236467,0.00001112068,0.000004233802,0.000008561842,0.00009487001,0.00004339575,0.00006734363,0.00001053484,0.04953525,0.0005171428,0.0009431395,0.948752],"study_design_scores_gemma":[0.0003714234,0.00002842146,0.0002776179,0.00008286711,0.00004229243,0.001588164,0.0000809549,0.3312135,0.1528233,0.003866494,0.5094703,0.0001546669],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002323376,0.0002180713,0.9911848,0.002157812,0.0006383305,0.00006518272,0.000009741281,0.00006186603,0.003340779],"genre_scores_gemma":[0.2524877,0.0001273791,0.7467836,0.0001126399,0.0003884471,6.469973e-8,0.00002107824,0.00002387271,0.00005522924],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9485974,"threshold_uncertainty_score":0.3796358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01252354266699032,"score_gpt":0.2706508899775248,"score_spread":0.2581273473105344,"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."}}