{"id":"W3210861119","doi":"","title":"Analysis and Prediction of Mid Winter Breakups on Northern Canadian Rivers Using Time Series Data Mining","year":2020,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Time series; Series (stratigraphy); Geography; Computer science; Geology; Machine learning","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":[0.0006760345,0.0007023002,0.0005092337,0.002255053,0.0009559751,0.001119122,0.0009157543,0.0005720009,0.0005561414],"category_scores_gemma":[0.002015042,0.0002691253,0.0006446019,0.002347151,0.0002718452,0.0003484508,0.0003373396,0.0005790378,0.0001657844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005152701,"about_ca_system_score_gemma":0.005870529,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.943722,"about_ca_topic_score_gemma":0.9461815,"domain_scores_codex":[0.9996649,0.00002068642,0.0000316416,0.00008138866,0.0001060174,0.00009526594],"domain_scores_gemma":[0.9986401,0.0003386025,0.0001822928,0.00005763573,0.0006137197,0.0001676533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003126712,0.0003302277,0.7970809,0.0000943875,0.0003382546,0.0004053295,0.000308173,0.1311166,0.002585262,0.0006095542,0.005732546,0.06108628],"study_design_scores_gemma":[0.00001629811,0.00003915184,0.6072232,0.00002613961,0.0000878879,0.00003659582,0.0006447455,0.3893341,0.0006999714,0.0002806047,0.00158193,0.00002931613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925957,0.000262921,0.001593201,0.0001282202,0.00001790434,0.00002680886,0.004605157,0.0001061823,0.0006639419],"genre_scores_gemma":[0.9867653,0.0002321909,0.002283956,0.00001970085,0.00001018274,0.00002651451,0.009521946,0.00001291725,0.001127297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05627799,"threshold_uncertainty_score":0.1132188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02121109306807459,"score_gpt":0.2116468634516607,"score_spread":0.1904357703835861,"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."}}