{"id":"W2145617653","doi":"10.1109/nafips.2003.1226788","title":"Robust centroid determination of noisy data using FCM and domain specific partitioning","year":2004,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"","keywords":"Centroid; Pattern recognition (psychology); Noise (video); Computer science; Weighting; Artificial intelligence; Fuzzy logic; Domain (mathematical analysis); Feature (linguistics); Partition (number theory); Metric (unit); Data mining; Mathematics; 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":[0.0001238374,0.00007863146,0.00009214318,0.00006657687,0.00004247209,0.00004352129,0.00008753462,0.00003923462,0.00001110882],"category_scores_gemma":[0.00001825313,0.00008478711,0.00001095475,0.0001119375,0.00004301711,0.0003389226,0.00003606433,0.00005537398,0.000005983534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000817176,"about_ca_system_score_gemma":0.00001003504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001530586,"about_ca_topic_score_gemma":0.00001599432,"domain_scores_codex":[0.9994167,0.00001141778,0.0001921715,0.0001589351,0.0001014697,0.000119292],"domain_scores_gemma":[0.9995095,0.00002080199,0.00003979469,0.0003634749,0.00003351017,0.00003298015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005228036,0.00002630694,0.0003442002,0.00008615,0.0000111203,0.000007647321,0.0003140718,0.09776528,0.882867,0.001191883,0.0001355803,0.01724556],"study_design_scores_gemma":[0.0006940237,0.00001453865,0.00694062,0.0001355363,0.00002121188,0.00005845565,0.0002404957,0.8983277,0.0908322,0.0009796601,0.001507447,0.0002480751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3737799,0.00008384942,0.6251786,0.00004350752,0.0000837465,0.00006870517,0.000005396646,0.00007638594,0.0006799659],"genre_scores_gemma":[0.6964365,0.00003827745,0.3034128,0.000004196369,0.00004426637,2.2518e-7,0.0000394291,0.00001559059,0.000008696245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8005624,"threshold_uncertainty_score":0.3457518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08282320723953959,"score_gpt":0.2467339486103486,"score_spread":0.163910741370809,"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."}}