{"id":"W2011961220","doi":"10.3390/rs6032372","title":"Multi-Temporal Polarimetric RADARSAT-2 for Land Cover Monitoring in Northeastern Ontario, Canada","year":2014,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Nipissing University","funders":"Northern Ontario Heritage Fund Corporation; Nipissing University","keywords":"Remote sensing; Polarimetry; Backscatter (email); Environmental science; Wetland; Land cover; Synthetic aperture radar; Vegetation (pathology); Physical geography; Geography; Land use; Scattering; Ecology; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0002111192,0.0002506724,0.0001591532,0.0007341043,0.001374851,0.0005151019,0.0003682914,0.0001191463,0.002212702],"category_scores_gemma":[0.0003628567,0.000145852,0.0001333234,0.001567606,0.0001646234,0.0002071556,0.0002363877,0.0001920765,0.0003588859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0132956,"about_ca_system_score_gemma":0.02326105,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9963087,"about_ca_topic_score_gemma":0.9990726,"domain_scores_codex":[0.999815,0.000009196782,0.000005797982,0.00002611822,0.00009803411,0.00004575168],"domain_scores_gemma":[0.9994938,0.00001454248,0.00003299421,0.000009036663,0.0003995622,0.00005009184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003747455,0.0001720732,0.6783006,0.0005579978,0.0001400977,0.0008541835,0.002744403,0.009174491,0.03539607,0.001581734,0.05042528,0.2202783],"study_design_scores_gemma":[0.00001949248,0.00002202993,0.9654011,0.00005295079,0.00002680929,0.00005914324,0.001297213,0.007461268,0.001421749,0.00008145648,0.02413294,0.00002386497],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9121488,0.002490748,0.007465102,0.00117072,0.0000736365,0.0004366802,0.0250039,0.0003805236,0.05082988],"genre_scores_gemma":[0.9471319,0.001406766,0.01008023,0.0001887621,0.00001275849,0.0001071714,0.0075486,0.00006836535,0.03345546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0132956,"threshold_uncertainty_score":0.09646678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01125234356663195,"score_gpt":0.2143915582138566,"score_spread":0.2031392146472247,"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."}}