{"id":"W4246005524","doi":"10.32920/ryerson.14644335.v1","title":"Fuzzy Similarity Measure and its Application to High Resolution Colour Remote Sensing Image Processing","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Measure (data warehouse); Artificial intelligence; Computer science; Cluster analysis; Similarity (geometry); Fuzzy logic; Similarity measure; Multivariate statistics; Pattern recognition (psychology); Fuzzy clustering; Focus (optics); Data mining; Image processing; Computer vision; Image (mathematics); Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005145903,0.000436916,0.0004553581,0.0002110626,0.0001715014,0.0004655492,0.0001617814,0.0005649218,0.000003223682],"category_scores_gemma":[0.0003198827,0.0005170971,0.00007021301,0.0003684642,0.0000376156,0.0002341103,0.0003012027,0.0008275183,0.00002076381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005236784,"about_ca_system_score_gemma":0.0001306376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001509072,"about_ca_topic_score_gemma":0.000175708,"domain_scores_codex":[0.9977224,0.0001067831,0.0005109349,0.000871858,0.0004074888,0.0003804902],"domain_scores_gemma":[0.9982035,0.00004525957,0.0001459674,0.0007055107,0.0007133167,0.0001864637],"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":[0.00002202938,0.00001605605,0.000004552924,0.001467045,0.00005436368,0.00002150348,0.000579256,0.05194307,0.6519179,0.0000417977,0.0007439735,0.2931884],"study_design_scores_gemma":[0.0001587352,0.000008053998,0.001815985,0.0005823355,0.00007894863,0.00003600603,0.0001082128,0.9541181,0.0415643,0.0006212704,0.0003443087,0.000563723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1179894,0.0007067968,0.872847,0.001416038,0.0004018998,0.001087456,0.00001275464,0.00115657,0.004382173],"genre_scores_gemma":[0.7830622,0.00008483831,0.2161886,0.00009286463,0.0002084233,0.00000287078,0.0001443416,0.0001024492,0.0001133664],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9021751,"threshold_uncertainty_score":0.9997281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02194577558023064,"score_gpt":0.2498957080574324,"score_spread":0.2279499324772017,"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."}}