{"id":"W2096688679","doi":"10.1016/j.rse.2003.11.010","title":"Mapping deciduous forest ice storm damage using Landsat and environmental data","year":2003,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":63,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Ministry of Natural Resources","keywords":"Remote sensing; Deciduous; Environmental science; Linear discriminant analysis; Computer science; Reference data; Multispectral image; Artificial intelligence; Data mining; Geography","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":[0.0004454229,0.0002604133,0.0002795723,0.00005903445,0.0002585083,0.00002891786,0.0002319934,0.0001034308,0.0001263891],"category_scores_gemma":[0.00004017414,0.0002667785,0.00004972552,0.0001109581,0.0004490338,0.0001628658,0.0004367271,0.000174164,0.00007565339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000245418,"about_ca_system_score_gemma":0.0000104834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008998218,"about_ca_topic_score_gemma":0.0001378244,"domain_scores_codex":[0.9980264,0.0001136316,0.0003777957,0.0006819962,0.0004233824,0.0003768008],"domain_scores_gemma":[0.998302,0.00008251929,0.0002063219,0.001235305,0.000001566591,0.0001723001],"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":[0.00004840197,0.0002929403,0.03709758,0.00005325225,0.0001347574,0.00008127569,0.002257001,0.026077,0.5844222,0.00004200105,0.0007552683,0.3487383],"study_design_scores_gemma":[0.001651245,0.0001438592,0.1723762,0.0001642595,0.0002028817,0.0006961155,0.002463346,0.6807219,0.01264319,0.001184896,0.126359,0.001393212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9558095,0.0001435689,0.04072345,0.0000650245,0.00006398501,0.0002587156,0.00002799208,0.00002678081,0.002881041],"genre_scores_gemma":[0.8840094,0.0001675584,0.1154796,0.00007172915,0.00002995289,3.124629e-8,0.00006192696,0.00003873499,0.0001410769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6546448,"threshold_uncertainty_score":0.9999784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02732239899868468,"score_gpt":0.2343944910414334,"score_spread":0.2070720920427487,"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."}}