{"id":"W2414888754","doi":"","title":"Data Driving Better Decisions In The Great Lakes-St. Lawrence River Basin","year":2014,"lang":"en","type":"article","venue":"CUNY Academic Works (City University of New York)","topic":"Transboundary Water Resource Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Drainage basin; Water resources; Structural basin; Environmental resource management; Surface water; Environmental science; Business; Environmental planning; Water resource management; Geography; Geology; Ecology; Environmental engineering; Cartography","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":[0.009956389,0.0005493592,0.0006567548,0.004376787,0.00326598,0.01013984,0.002060744,0.001464005,0.009654826],"category_scores_gemma":[0.04738989,0.0005916241,0.0006921063,0.009115092,0.002213237,0.007649634,0.003980997,0.002410662,0.002079114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01379093,"about_ca_system_score_gemma":0.02832253,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6270638,"about_ca_topic_score_gemma":0.682954,"domain_scores_codex":[0.9928223,0.002779057,0.0006756724,0.0009734433,0.002201065,0.0005484362],"domain_scores_gemma":[0.9711909,0.01129434,0.002310391,0.00207727,0.01174105,0.001385999],"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":[0.0002350232,0.0002904388,0.1957417,0.001051904,0.0003054524,0.0005560051,0.006047013,0.02409506,0.000350503,0.1292044,0.4279672,0.2141555],"study_design_scores_gemma":[0.000125146,0.00007319128,0.08800218,0.002182638,0.0001698381,0.00009778173,0.0161331,0.03444924,0.001464266,0.1227794,0.7342263,0.0002967565],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2366792,0.01378636,0.03244745,0.2285375,0.001839857,0.0008858782,0.1116241,0.003900068,0.3702996],"genre_scores_gemma":[0.817452,0.01057963,0.05552166,0.01034381,0.0004815941,0.0005517129,0.07209492,0.0007253755,0.03224934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3729362,"threshold_uncertainty_score":0.7502651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07290059418390668,"score_gpt":0.2786392532789892,"score_spread":0.2057386590950825,"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."}}