{"id":"W2752105008","doi":"10.1080/01431161.2017.1372863","title":"Enhanced decision tree ensembles for land-cover mapping from fully polarimetric SAR data","year":2017,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Ottawa","funders":"Agriculture and Agri-Food Canada; Jet Propulsion Laboratory; Iran National Science Foundation; European Space Agency","keywords":"Random forest; Land cover; Computer science; Decision tree; Polarimetry; Tree (set theory); Synthetic aperture radar; Filter (signal processing); Feature (linguistics); Data mining; Remote sensing; Artificial intelligence; Pattern recognition (psychology); Mathematics; Land use; Geography; Computer vision","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":[0.001734531,0.0009348641,0.0009529791,0.0009341781,0.0003710563,0.0005540559,0.0007183343,0.0005348756,0.0005678104],"category_scores_gemma":[0.002811236,0.0003227794,0.001157263,0.0009837745,0.0001656036,0.00101463,0.000660777,0.0009553668,0.0002951225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003694684,"about_ca_system_score_gemma":0.0004879581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003926087,"about_ca_topic_score_gemma":0.004089248,"domain_scores_codex":[0.9993728,0.0002256907,0.00003972046,0.0001135052,0.0001676943,0.00008062967],"domain_scores_gemma":[0.9989286,0.0005292688,0.00008171698,0.0001093471,0.0002968884,0.00005418192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001887829,0.00009261569,0.002665382,0.00006551298,0.0002026435,0.00007615628,0.00008880607,0.7016155,0.008550645,0.00188436,0.001582165,0.2829874],"study_design_scores_gemma":[0.000003872661,0.00002020563,0.0004370972,0.000003478967,0.00001737176,0.000009954169,0.000008548489,0.9968693,0.00125848,0.001052845,0.0003133721,0.000005524001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09761798,0.0007809446,0.899484,0.000125331,0.00008457511,0.00004939776,0.0002256946,0.0008024914,0.0008294893],"genre_scores_gemma":[0.7305228,0.0004024844,0.2664261,0.0001007136,0.00008798685,0.0001143104,0.001214113,0.00008823979,0.001043323],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003926087,"threshold_uncertainty_score":0.009173155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03124478095581361,"score_gpt":0.2977821103411442,"score_spread":0.2665373293853306,"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."}}