{"id":"W1618555190","doi":"","title":"ANALYSIS OF ASAR IMAGERY FOR HYDROLOGICAL APPLICATIONS IN SARDINIA, ITALY","year":2005,"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":"Université du Québec","funders":"","keywords":"Remote sensing; Terrain; Environmental science; Irrigation; Geology; Hydrology (agriculture); Geography; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001151468,0.00004913563,0.0001407067,0.00006345944,0.00002468276,0.000004065053,0.00006898667,0.00004156254,0.0001492873],"category_scores_gemma":[0.00001336034,0.0000354446,0.0001086693,0.0004587312,0.00007053361,0.00004426944,0.00003207329,0.00003174484,0.00003384547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003481578,"about_ca_system_score_gemma":0.000001751586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002142708,"about_ca_topic_score_gemma":0.002099674,"domain_scores_codex":[0.9994982,0.000009639415,0.000148284,0.0001546681,0.00008054091,0.000108663],"domain_scores_gemma":[0.9997211,0.00008392676,0.00003211078,0.0001327142,0.000003085249,0.00002712042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001804214,0.0002962771,0.7433348,0.000003663587,0.0001569209,0.000001076358,0.0003294142,0.03888887,0.02051477,0.0001989821,0.001090242,0.195167],"study_design_scores_gemma":[0.0001231055,0.00001701167,0.9576535,6.939633e-7,0.0001345368,5.228963e-7,0.00006612603,0.03113799,0.002564688,0.0002338853,0.007987734,0.00008023786],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8863239,0.00001976247,0.008662882,0.0004155076,0.000007048626,0.0001634579,0.000001601679,0.00001539963,0.1043904],"genre_scores_gemma":[0.9911358,0.000003137775,0.008260217,0.000216994,0.00001759087,0.000002648278,0.000008472085,0.000002602524,0.0003525501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2143187,"threshold_uncertainty_score":0.1634591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0102427002403474,"score_gpt":0.2511366166121695,"score_spread":0.2408939163718221,"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."}}