{"id":"W4234152333","doi":"10.5194/isprsarchives-xli-b1-101-2016","title":"NEW MICROWAVE-BASED MISSIONS APPLICATIONS FOR RAINFED CROPS CHARACTERIZATION","year":2016,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Regional Development Fund; Canadian Space Agency; Ministerio de Economía y Competitividad","keywords":"Remote sensing; Environmental science; Radar; Synthetic aperture radar; Satellite; Water content; Polarimetry; Reflectometry; Vegetation (pathology); Computer science; Geography; Geology; Scattering; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0005091024,0.0004870472,0.0002728886,0.0009872726,0.00009748105,0.0004139941,0.0002678804,0.0002319019,0.001059248],"category_scores_gemma":[0.000304218,0.0001244544,0.0001984461,0.001027,0.00008341144,0.0004558416,0.0002338935,0.0002022945,0.0003982926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001476835,"about_ca_system_score_gemma":0.0000962019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000547117,"about_ca_topic_score_gemma":0.001244643,"domain_scores_codex":[0.9998416,0.00004637029,0.000007600261,0.00004172071,0.00004455791,0.00001809201],"domain_scores_gemma":[0.9998253,0.00003026765,0.00002918071,0.00003341064,0.00006639432,0.00001544741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007141467,0.0002987848,0.060994,0.000481979,0.0001203024,0.000175578,0.0002376316,0.01496359,0.5443637,0.0009116575,0.001425514,0.3753131],"study_design_scores_gemma":[0.0001177067,0.001216068,0.4058047,0.0001026547,0.0004104958,0.0008100947,0.0008852956,0.279608,0.2793036,0.001901689,0.02971333,0.0001263891],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8564548,0.002066139,0.1314316,0.0001220548,0.0000825032,0.0001750321,0.002589415,0.001306269,0.00577209],"genre_scores_gemma":[0.8733225,0.0007638272,0.122601,0.00004832295,0.00004929135,0.00009665175,0.001458952,0.00004780111,0.001611587],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001059248,"threshold_uncertainty_score":0.003543496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01352654562743293,"score_gpt":0.2444927185887724,"score_spread":0.2309661729613395,"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."}}