{"id":"W4402260276","doi":"10.1109/igarss53475.2024.10642495","title":"Canola Phenology Mapping Using Optical and Synthetic Aperture Radar (Sar) in Canada","year":2024,"lang":"en","type":"article","venue":"","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Saskatchewan","funders":"","keywords":"Synthetic aperture radar; Remote sensing; Canola; Inverse synthetic aperture radar; Radar imaging; Side looking airborne radar; Phenology; Computer science; Environmental science; Radar; Meteorology; Bistatic radar; Geography; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005904815,0.00008941214,0.0001044871,0.0000277652,0.00004321966,0.00002264383,0.00005014641,0.00005334812,0.00009015879],"category_scores_gemma":[0.00002039958,0.00006797818,0.00001266076,0.000156025,0.00008227945,0.00004902296,0.00008247785,0.0001499226,0.00001976654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005047733,"about_ca_system_score_gemma":0.0001005955,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8780036,"about_ca_topic_score_gemma":0.9621452,"domain_scores_codex":[0.9992811,0.00001986298,0.0001078758,0.0002457446,0.0001167608,0.0002286179],"domain_scores_gemma":[0.9997395,0.0001040054,0.00000718426,0.00008697788,0.000001082117,0.00006128765],"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":[0.00002434231,0.0000463968,0.08155809,0.000149317,0.000081422,0.00368351,0.002767766,0.002153088,0.1754148,0.001715099,0.003870398,0.7285358],"study_design_scores_gemma":[0.0006350256,0.00006360071,0.2483488,0.0005453537,0.00007255496,0.002506721,0.005595264,0.5676969,0.003914695,0.001685152,0.1675773,0.00135853],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9548517,0.0005330159,0.0004890871,0.001460298,0.0002506469,0.00006521857,4.200113e-7,0.00002393038,0.04232562],"genre_scores_gemma":[0.9940131,0.00001547485,0.005066522,0.0006562066,0.00002005825,7.519247e-8,6.28756e-7,0.000009215441,0.0002187083],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7271772,"threshold_uncertainty_score":0.277207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00910583122241523,"score_gpt":0.1963242633955284,"score_spread":0.1872184321731132,"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."}}