{"id":"W4403406950","doi":"10.1080/07038992.2024.2407163","title":"Monitoring Crop Condition at Field Scales and at a Daily Time Step Using Synthetic Aperture Radar (SAR)","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","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","funders":"","keywords":"Synthetic aperture radar; Remote sensing; Geography; Crop; Scale (ratio); Cartography; Environmental science; Forestry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0002322178,0.00018297,0.0002285444,0.0001552424,0.0004356822,0.0001481668,0.00007783317,0.0001449032,0.00007150471],"category_scores_gemma":[0.0001152892,0.0001597347,0.0001037668,0.0001843613,0.0001930979,0.0001936315,0.00005045312,0.0003057082,0.00006564178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009974736,"about_ca_system_score_gemma":0.00009115706,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007286902,"about_ca_topic_score_gemma":0.01871013,"domain_scores_codex":[0.9988046,0.00006701261,0.0002811048,0.0002404004,0.0002447333,0.0003621715],"domain_scores_gemma":[0.9990616,0.0001815429,0.000107937,0.0001444872,0.00002357681,0.0004808656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002265845,0.000001675944,0.001115532,0.00003951844,0.00005756643,0.003062773,0.001079637,0.0001535891,0.1314512,8.136619e-7,0.002358291,0.8606567],"study_design_scores_gemma":[0.00148596,0.000494142,0.03129465,0.01066956,0.0008116005,0.08427081,0.001460408,0.3796167,0.09287412,0.0008433455,0.393901,0.002277731],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897885,0.002801961,0.001957492,0.001101599,0.0008856339,0.00007159106,0.000002387485,0.00001698087,0.003373876],"genre_scores_gemma":[0.9691182,0.00005689416,0.02947572,0.0002804819,0.0003839676,1.228863e-9,0.000001220034,0.00003563598,0.0006478396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.858379,"threshold_uncertainty_score":0.9993237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008674970645243665,"score_gpt":0.2180928080435607,"score_spread":0.209417837398317,"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."}}