{"id":"W2995237149","doi":"","title":"Application of Multi-Frequency and Multi-Temporal Polarimetric SAR for Operational Crop Inventories in Canada","year":2008,"lang":"en","type":"article","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Remote sensing; Environmental science; Computer science; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005452047,0.0002829962,0.0002447758,0.001667378,0.00139062,0.001370057,0.0007117538,0.0001949306,0.001183721],"category_scores_gemma":[0.0009480909,0.0002352524,0.0002855514,0.002567995,0.0003044211,0.0004176031,0.0003434237,0.0002655137,0.0001245856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02397417,"about_ca_system_score_gemma":0.02766982,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9964768,"about_ca_topic_score_gemma":0.99793,"domain_scores_codex":[0.9995773,0.00002704396,0.00001975053,0.0000706319,0.0001957669,0.0001094196],"domain_scores_gemma":[0.999099,0.0000695918,0.00003617478,0.00001854255,0.0006977572,0.0000790111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000859445,0.0001982887,0.598293,0.0005018085,0.0003951476,0.001243273,0.002246078,0.07969559,0.02975723,0.00481276,0.007041598,0.2749557],"study_design_scores_gemma":[0.00005146932,0.00005072697,0.8931368,0.00005700803,0.0001215974,0.0001758267,0.002474773,0.07833353,0.008100518,0.0003792598,0.0170423,0.00007614265],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9817542,0.001069057,0.002899596,0.0003532261,0.00002981194,0.00007198314,0.00362393,0.0001330864,0.01006501],"genre_scores_gemma":[0.9899622,0.0005779926,0.004749868,0.00003329237,0.000003845204,0.00001001992,0.001077936,0.00002346509,0.003561308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02397417,"threshold_uncertainty_score":0.1739456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01741205267749194,"score_gpt":0.2303883641870884,"score_spread":0.2129763115095964,"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."}}