{"id":"W4210920760","doi":"10.3389/frwa.2022.803869","title":"Navigating Great Lakes Hydroclimate Data","year":2022,"lang":"en","type":"article","venue":"Frontiers in Water","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"NOAA Great Lakes Environmental Research Laboratory; National Oceanic and Atmospheric Administration","keywords":"Structural basin; Natural (archaeology); Geography; Environmental resource management; Environmental science; Geology; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001793468,0.0003399234,0.0002976342,0.002153315,0.0006613482,0.001581209,0.001050761,0.0005190933,0.002662781],"category_scores_gemma":[0.009654767,0.0003330685,0.0004120405,0.003929621,0.0003749185,0.001987082,0.002484031,0.0008826076,0.001398618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001165262,"about_ca_system_score_gemma":0.003206146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1584731,"about_ca_topic_score_gemma":0.2514642,"domain_scores_codex":[0.9989028,0.0003615197,0.00008739463,0.0002240853,0.0003572227,0.00006696766],"domain_scores_gemma":[0.9973248,0.0007763303,0.0002622188,0.0005472338,0.0008012116,0.0002880998],"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":[0.0001596761,0.0001324371,0.2121551,0.00085528,0.0003690184,0.0007619397,0.004677156,0.05407051,0.003431652,0.02227076,0.5231652,0.1779512],"study_design_scores_gemma":[0.00007710318,0.00003944699,0.08336857,0.0004719716,0.00007179936,0.0001165122,0.003370979,0.1351185,0.003198627,0.02278681,0.7511884,0.0001913563],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1960148,0.002079069,0.06439591,0.01960951,0.000463044,0.0005857779,0.6364247,0.01878247,0.06164477],"genre_scores_gemma":[0.4634495,0.002015099,0.1582262,0.001314238,0.0001278473,0.0007405579,0.3665812,0.001464339,0.006081027],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1584731,"threshold_uncertainty_score":0.3151013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0150812534673454,"score_gpt":0.2371756022616143,"score_spread":0.2220943487942689,"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."}}