{"id":"W4239215490","doi":"10.32920/ryerson.14644335","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); Computer science; Fuzzy logic; Artificial intelligence; Similarity measure; Cluster analysis; Similarity (geometry); Multivariate statistics; Pattern recognition (psychology); Fuzzy clustering; Data mining; Focus (optics); Computer vision; Image (mathematics); Remote sensing; Geography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001185485,0.0003251837,0.0006107227,0.002706247,0.0004036633,0.001358101,0.0006037756,0.0009432296,0.001114794],"category_scores_gemma":[0.004126534,0.0001708319,0.0008095245,0.002780278,0.0009407485,0.001219483,0.0006300284,0.0007445311,0.0003358494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005615948,"about_ca_system_score_gemma":0.0004742447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001425703,"about_ca_topic_score_gemma":0.0008023886,"domain_scores_codex":[0.9988711,0.0002492049,0.00009027275,0.0001879428,0.0005600688,0.00004126399],"domain_scores_gemma":[0.998908,0.0005138145,0.00012848,0.00009285094,0.0003146623,0.00004224208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001715584,0.00011408,0.002905277,0.0005954539,0.0001797941,0.0005590097,0.0004989539,0.1552074,0.04949721,0.1633341,0.002752278,0.6241849],"study_design_scores_gemma":[0.0000107006,0.0001350088,0.003972619,0.00004292681,0.00003906056,0.0008483164,0.0001127009,0.9084419,0.01188841,0.06835005,0.006104722,0.00005357144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01559641,0.00130406,0.9801631,0.0001812608,0.00007052816,0.00003545506,0.00003930162,0.0001125849,0.002497382],"genre_scores_gemma":[0.4290951,0.00186312,0.5659743,0.0001181643,0.0002431634,0.0001031455,0.000150872,0.00004828313,0.002403907],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002706247,"threshold_uncertainty_score":0.006269574,"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."}}