{"id":"W2064875349","doi":"10.5589/m02-096","title":"Disturbance recognition in the boreal forest using radar and Landsat-7","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"NASA Headquarters; National Aeronautics and Space Administration","keywords":"Remote sensing; Thematic Mapper; Forestry; Geography; Radar; Environmental science; Cartography; Geology; Satellite imagery; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004605746,0.00007173462,0.0000953554,0.00006492891,0.000172186,0.00005483176,0.00006019402,0.00003842502,0.000006514374],"category_scores_gemma":[0.0001043328,0.00005555538,0.00002922829,0.0002154488,0.0001234657,0.00008329143,0.000003454727,0.0001844482,0.000004068284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001438774,"about_ca_system_score_gemma":0.00008325662,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02755671,"about_ca_topic_score_gemma":0.1139845,"domain_scores_codex":[0.9993262,0.00009711411,0.0001803337,0.00009432219,0.0001111442,0.0001908867],"domain_scores_gemma":[0.9995724,0.00004535467,0.0001026272,0.0001078544,0.00001480472,0.0001569384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00000704274,0.000005157106,0.01259109,0.000005105446,0.000007958603,0.0002808833,0.002509275,0.0002716169,0.0007006301,0.00003873219,0.0008055334,0.982777],"study_design_scores_gemma":[0.002123359,0.0001966181,0.5705763,0.0006800267,0.0001654598,0.02235147,0.005823441,0.05709336,0.001507627,0.03316953,0.3052797,0.001033179],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9811767,0.0001164171,0.00709347,0.0004868229,0.00008457735,0.00006877271,0.000001339564,0.000001820402,0.01097014],"genre_scores_gemma":[0.9431973,0.00001260134,0.056493,0.0002305289,0.00004458148,2.345207e-9,0.000001125007,0.000007340854,0.00001352468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9817438,"threshold_uncertainty_score":0.9789189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0183990700920793,"score_gpt":0.2193540864165932,"score_spread":0.2009550163245139,"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."}}