{"id":"W2263821834","doi":"","title":"Flood hydrograph classification using functional data analysis","year":2013,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Hydrograph; Flood myth; Computer science; Cartography; Geography; Data mining; Archaeology","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":[0.0008878764,0.0005682511,0.0005251244,0.004221745,0.0003571579,0.0009895491,0.0004763601,0.0005798783,0.001787879],"category_scores_gemma":[0.002618563,0.0001997809,0.0009503632,0.002171612,0.0003554557,0.0009543516,0.0006340892,0.0004774743,0.0007028469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004286845,"about_ca_system_score_gemma":0.0005088669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005329846,"about_ca_topic_score_gemma":0.003491692,"domain_scores_codex":[0.9995852,0.0001091077,0.00004406531,0.0001034436,0.00009086651,0.00006723798],"domain_scores_gemma":[0.9988379,0.0004940777,0.00009281149,0.0001568037,0.0003537224,0.0000647899],"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":[0.0004444266,0.0003183321,0.05976225,0.0002125476,0.0002277189,0.0002819574,0.0002584558,0.1013929,0.01748458,0.007455249,0.005863087,0.8062984],"study_design_scores_gemma":[0.000006954501,0.0000731506,0.02184874,0.0000229419,0.0000402037,0.00008817497,0.000170158,0.9650662,0.003786812,0.007046125,0.001826643,0.00002388344],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2938446,0.0004339265,0.6975883,0.0004481433,0.0001180922,0.0001307601,0.002914816,0.001568644,0.002952651],"genre_scores_gemma":[0.9080937,0.0002134078,0.0860906,0.000039894,0.00005910953,0.0001024594,0.003463557,0.00008329733,0.001853911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005329846,"threshold_uncertainty_score":0.01059765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05569828216295755,"score_gpt":0.2460018740419181,"score_spread":0.1903035918789606,"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."}}