{"id":"W2144218027","doi":"10.1109/igarss.2004.1369962","title":"The effect of some internal neural network parameters on SAR texture classification performance","year":2004,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec; Institut National de la Recherche Scientifique","funders":"","keywords":"Artificial neural network; Computer science; Backpropagation; Artificial intelligence; Contextual image classification; Data classification; Pattern recognition (psychology); Machine learning; Process (computing); Data mining; Image (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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001971952,0.0001320511,0.000117944,0.00003353581,0.00007586661,0.00004149253,0.0001597381,0.0000626826,0.000002023431],"category_scores_gemma":[0.0000432774,0.00008460634,0.00005725262,0.0001266126,0.00006962837,0.000132382,0.00001045457,0.000208598,0.00004589894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001049394,"about_ca_system_score_gemma":0.000005637169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005283058,"about_ca_topic_score_gemma":0.000002493173,"domain_scores_codex":[0.9992805,0.00003688358,0.0002084496,0.0001247074,0.0001537195,0.0001957602],"domain_scores_gemma":[0.9993928,0.0001709051,0.00005586024,0.0003263079,0.00002079463,0.00003331034],"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":[0.0000717176,0.000006024149,0.001099812,0.00004506594,0.0000299731,8.798044e-7,0.0000473562,0.8304506,0.01375704,0.0005120959,0.0008830751,0.1530964],"study_design_scores_gemma":[0.0004052369,0.0002774954,0.04709923,0.00008351573,0.00001559031,0.000008322466,0.00001124991,0.895005,0.05626539,0.000104886,0.0005897694,0.0001342634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908306,0.00009097895,0.005615888,0.0002841124,0.0006049196,0.0002036474,5.212694e-7,0.0001765539,0.00219277],"genre_scores_gemma":[0.9990614,0.00005617279,0.0006088747,0.00003472996,0.0001150784,0.000002734742,0.00000448947,0.00002386491,0.00009265893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1529621,"threshold_uncertainty_score":0.3450146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01045861435825262,"score_gpt":0.2165962809908026,"score_spread":0.20613766663255,"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."}}