{"id":"W2995023240","doi":"10.22126/ges.2019.4294.2071","title":"استخراج پتانسیل سیل خیزی حوضه سیمینه رود با کمک تصاویر ماهواره ای، شاخص رطوبت توپوگرافی و ویژگیهای مورفولوژیکی","year":2019,"lang":"fa","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007907636,0.0003250396,0.0002471739,0.0008696475,0.0007281406,0.001791058,0.00027169,0.0005203536,0.02666516],"category_scores_gemma":[0.001280034,0.0003258105,0.0002891408,0.001532296,0.0007422468,0.0009115175,0.0006705438,0.0009008389,0.01164872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000855667,"about_ca_system_score_gemma":0.001928548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00453816,"about_ca_topic_score_gemma":0.007333782,"domain_scores_codex":[0.999375,0.00007363364,0.00006217438,0.00007962197,0.000343949,0.00006555892],"domain_scores_gemma":[0.9993774,0.0001283194,0.0001301872,0.00003834826,0.0002834423,0.00004234415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009454681,0.0002708894,0.01662836,0.001296906,0.00006602884,0.005399395,0.007161084,0.003701093,0.1375096,0.05527718,0.02500484,0.7467393],"study_design_scores_gemma":[0.0001129469,0.0004717619,0.1221433,0.0006476719,0.00009015857,0.004045723,0.01089868,0.00995225,0.08642738,0.03172811,0.7332715,0.000210454],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4967404,0.005628526,0.1894549,0.003747485,0.00120533,0.0007437264,0.002910685,0.001244423,0.2983246],"genre_scores_gemma":[0.6742936,0.009131085,0.1339783,0.0007231374,0.0003612969,0.001159731,0.003316509,0.0008229839,0.1762134],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02666516,"threshold_uncertainty_score":0.08920383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1755006083859913,"score_gpt":0.5400262434695607,"score_spread":0.3645256350835694,"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."}}