{"id":"W2187380372","doi":"","title":"Mixture of Latent Variable Models for Remotely Sensed Image Processing","year":2014,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; University of Waterloo","keywords":"Latent variable; Variable (mathematics); Image processing; Computer science; Artificial intelligence; Remote sensing; Computer vision; Environmental science; Geography; Image (mathematics); Cartography; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002817947,0.001270756,0.001192276,0.001442645,0.0004280366,0.002359973,0.002730001,0.001841574,0.003969835],"category_scores_gemma":[0.006541335,0.0006978738,0.00210942,0.00227232,0.001332309,0.002389875,0.002278277,0.004009929,0.001744885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001328122,"about_ca_system_score_gemma":0.001217085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005542827,"about_ca_topic_score_gemma":0.004812942,"domain_scores_codex":[0.9983373,0.0008132143,0.00006543709,0.0003127353,0.0003564153,0.0001148621],"domain_scores_gemma":[0.9977457,0.001510658,0.0002436467,0.000190844,0.0002509232,0.00005819109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007346184,0.00007834782,0.001534828,0.0004145849,0.0002512758,0.0001617515,0.0002730511,0.3399412,0.001927353,0.5432227,0.009054064,0.1030674],"study_design_scores_gemma":[0.000006853452,0.00001821489,0.0003104012,0.0000582968,0.00003005995,0.00004645074,0.00002613555,0.8464884,0.0002599293,0.1440887,0.008640131,0.00002655522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000857062,0.002000876,0.9950367,0.0003579099,0.00007976402,0.00002463271,0.0001631714,0.0002069409,0.001272896],"genre_scores_gemma":[0.2200103,0.01355562,0.7477757,0.0006993482,0.001199293,0.0007358918,0.001950806,0.0004410374,0.01363202],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005542827,"threshold_uncertainty_score":0.01490289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01169294876436274,"score_gpt":0.1929489134520351,"score_spread":0.1812559646876723,"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."}}