{"id":"W6982065887","doi":"","title":"Great Lakes Update, Volume 192 : 2014 Annual Summary","year":2016,"lang":"en","type":"other","venue":"US Army Corps of Engineers: Engineer Research and Development Center (Knowledge Core)","topic":"Ancient Near East History","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hydrology (agriculture); Spring (device); Water level; Period (music); Water year; Shelf ice; Volume (thermodynamics)","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":[0.001524177,0.001239215,0.0008015083,0.005817568,0.0005315089,0.002502743,0.001456895,0.0007770772,0.2059153],"category_scores_gemma":[0.009073065,0.0004943404,0.0007314382,0.009179158,0.0002586475,0.002626662,0.001993755,0.001096686,0.1650863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001396761,"about_ca_system_score_gemma":0.004986009,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03162447,"about_ca_topic_score_gemma":0.03293518,"domain_scores_codex":[0.998774,0.0001098288,0.0001859045,0.0001261762,0.0007172207,0.00008687781],"domain_scores_gemma":[0.9962682,0.0002997869,0.0003441303,0.0001777399,0.00259855,0.000311491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00001361449,0.000006548752,0.0002813786,0.0001862102,0.000004289107,0.000006680143,0.000006640889,0.00003110939,0.00001549483,0.0001441431,0.9732823,0.02602163],"study_design_scores_gemma":[0.00001220331,0.000005717617,0.002510401,0.0002878529,0.000006869796,0.00002370063,0.00001464676,0.00002668019,0.00003091911,0.0001847511,0.9968904,0.000005976454],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.001388226,0.02521271,0.002119673,0.01199458,0.01084499,0.0005998129,0.714793,0.004064084,0.228983],"genre_scores_gemma":[0.005033957,0.04790727,0.004242822,0.004341202,0.006429484,0.00143812,0.6252697,0.001525597,0.3038119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9683755,"threshold_uncertainty_score":0.6888554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03843236201661235,"score_gpt":0.2651722633899055,"score_spread":0.2267399013732931,"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."}}