{"id":"W7294557","doi":"10.4095/219589","title":"The Potential of RADARSAT-2 for Crop Mapping and Assessing Crop Condition","year":2000,"lang":"en","type":"report","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Crop; Environmental science; Agronomy; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008464777,0.0005490574,0.0002596764,0.001092001,0.0001515853,0.0007586791,0.0004629642,0.0003673731,0.00256584],"category_scores_gemma":[0.001131105,0.0001690625,0.0002197572,0.001056705,0.0001481306,0.0007548929,0.0003703515,0.0002887539,0.001264941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003601687,"about_ca_system_score_gemma":0.0003456207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005201701,"about_ca_topic_score_gemma":0.008127856,"domain_scores_codex":[0.9996651,0.0001278958,0.00001341068,0.00005561077,0.0001097425,0.00002815788],"domain_scores_gemma":[0.9993538,0.0001927014,0.00007590424,0.0001107338,0.0002300872,0.00003679865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003737665,0.0001590208,0.04903285,0.0003548185,0.000100496,0.0002400208,0.0001356039,0.03491708,0.08925442,0.00351061,0.01184733,0.810074],"study_design_scores_gemma":[0.0001669426,0.001021378,0.34718,0.0003215707,0.0002865586,0.001509543,0.0005222764,0.3669161,0.114193,0.01865976,0.1488219,0.0004010589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4664328,0.01393608,0.3320132,0.005584306,0.0006152656,0.0005484161,0.0218832,0.005000121,0.1539867],"genre_scores_gemma":[0.7871078,0.005243733,0.1874796,0.0008489635,0.0003358398,0.0001413547,0.00861468,0.0001661678,0.01006202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005201701,"threshold_uncertainty_score":0.01034284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617793021293359,"score_gpt":0.2753190576053175,"score_spread":0.2591411273923839,"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."}}