{"id":"W2248879593","doi":"","title":"Building Great Lakes Resiliency to Eutrophication: Lessons to inform adaptive governance of the nearshore areas of the Laurentian Great Lakes.","year":2015,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Transboundary Water Resource Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"New York Sea Grant, State University of New York; Natural Sciences and Engineering Research Council of Canada; Michigan Sea Grant, University of Michigan","keywords":"Eutrophication; Corporate governance; Geography; Environmental planning; Environmental resource management; Oceanography; Environmental science; Ecology; Geology; Business; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.005438109,0.0003858706,0.0002109386,0.001240626,0.003410647,0.005928115,0.001514109,0.001419262,0.003203953],"category_scores_gemma":[0.008541204,0.0002215473,0.0003113081,0.001030988,0.008923272,0.006506494,0.008928562,0.00191681,0.0002072348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009382467,"about_ca_system_score_gemma":0.02060399,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05634466,"about_ca_topic_score_gemma":0.1678087,"domain_scores_codex":[0.9978898,0.001265337,0.00005847986,0.0001770603,0.0002480184,0.0003612289],"domain_scores_gemma":[0.9965246,0.001471473,0.0003419396,0.0002646869,0.0004886706,0.0009085633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00006707018,0.0001352415,0.07912269,0.001089955,0.0001478934,0.002563505,0.1745097,0.009432496,0.003068917,0.34707,0.06890017,0.3138923],"study_design_scores_gemma":[0.00002059034,0.0001591859,0.08605143,0.001775972,0.00005752684,0.0002466959,0.2693253,0.005603238,0.001489243,0.200055,0.4351109,0.0001048468],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3966099,0.01230182,0.03874135,0.3725369,0.000518786,0.0004164988,0.0003577079,0.0004445631,0.1780725],"genre_scores_gemma":[0.9698747,0.003778978,0.01562827,0.003611553,0.00006297229,0.0001854755,0.0001410188,0.00004568709,0.006671447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9436554,"threshold_uncertainty_score":0.1120334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02223612301461668,"score_gpt":0.255235602138078,"score_spread":0.2329994791234613,"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."}}