{"id":"W229809679","doi":"","title":"RADARSAT SAR data for landuse/land-cover classification in the rural-urban fringe of the greater Toronto area","year":2005,"lang":"en","type":"article","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Land cover; Land use; Remote sensing; Environmental science; Geography; Cartography; Engineering; Civil engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001896083,0.0001006366,0.0001053776,0.00001570799,0.00003894035,0.00001961316,0.0005741772,0.00006844629,0.00009213018],"category_scores_gemma":[0.00001419138,0.00005279059,0.00003731971,0.00006300205,0.00002420115,0.0001341419,0.00004422895,0.0000628995,0.000004446154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006365528,"about_ca_system_score_gemma":0.000007561714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002630703,"about_ca_topic_score_gemma":0.0003215402,"domain_scores_codex":[0.9994402,0.00001470634,0.0001885587,0.0001247056,0.0001093286,0.0001225109],"domain_scores_gemma":[0.9988763,0.0000901507,0.00003036702,0.0009732032,0.00001649034,0.00001350201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003909182,0.0002113135,0.02276854,0.0001086942,0.0001150306,3.488214e-7,0.002790473,0.00005727959,0.004535809,0.01889393,0.292287,0.6581925],"study_design_scores_gemma":[0.0001717009,0.000007495215,0.008235861,0.0000202323,0.0000179393,0.000002832345,0.0001322217,0.02314598,0.005423322,0.0001385857,0.9626098,0.00009404119],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2101015,0.003164059,0.6493543,0.0127254,0.0003222412,0.005273259,0.0006900399,0.00103841,0.1173308],"genre_scores_gemma":[0.9591464,0.00009915896,0.0401471,0.0001466894,0.0001019029,0.00001807971,0.00004677319,0.0000195731,0.0002742576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.749045,"threshold_uncertainty_score":0.2152738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03280706759641448,"score_gpt":0.2498201848577189,"score_spread":0.2170131172613044,"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."}}