{"id":"W4280607552","doi":"10.5194/isprs-annals-v-3-2022-405-2022","title":"SOYBEAN YIELD FORECAST USING DUAL-POLARIMETRIC C-BAND SYNTHETIC APERTURE RADAR","year":2022,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"National Aeronautics and Space Administration","keywords":"Synthetic aperture radar; Mean squared error; Radar; Remote sensing; Interferometric synthetic aperture radar; Yield (engineering); C band; Environmental science; Growing season; Meteorology; Mathematics; Statistics; Computer science; Geography; Agronomy; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0002204209,0.0004175718,0.0002256576,0.0003231708,0.00007018731,0.0002840864,0.0001524744,0.000186456,0.0002963052],"category_scores_gemma":[0.0003636192,0.0001076442,0.0002097918,0.000219513,0.0000623133,0.0002566878,0.0001278429,0.0001883172,0.00016844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003687318,"about_ca_system_score_gemma":0.0002897619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01311811,"about_ca_topic_score_gemma":0.01171841,"domain_scores_codex":[0.9999346,0.00001037572,0.000003442646,0.00002249118,0.0000194718,0.000009649323],"domain_scores_gemma":[0.9998794,0.0000244439,0.00002043841,0.000008758144,0.00005635343,0.00001070324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004384571,0.0002169586,0.1039519,0.00009406946,0.0001026975,0.0002345119,0.0000424943,0.7229477,0.08646391,0.0003399215,0.002045626,0.08312169],"study_design_scores_gemma":[0.000007943332,0.00002921892,0.02242864,0.000003123048,0.00001080877,0.000009044833,0.00001185748,0.9730561,0.004214387,0.00005334287,0.0001674152,0.000008021658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9757394,0.0001389275,0.02170218,0.00007212345,0.0000297652,0.00001080254,0.0005014216,0.0002806945,0.001524816],"genre_scores_gemma":[0.994845,0.00004514789,0.004471287,0.000005758343,0.000004748842,0.000002772835,0.0003751232,0.000006738467,0.0002434716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01311811,"threshold_uncertainty_score":0.02608353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04246571515508216,"score_gpt":0.2630223165739584,"score_spread":0.2205566014188763,"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."}}