{"id":"W6926332663","doi":"10.25384/sage.c.5100269","title":"Watershed or bank-to-bank? Scales of governance and the geographic definition of Great Lakes Areas of Concern","year":2020,"lang":"en","type":"other","venue":"Sage Journals Data","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Watershed; Corporate governance; Scale (ratio); Stakeholder; Scholarship; Watershed management","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":[0.001532692,0.000122388,0.0001580891,0.001108202,0.001027004,0.004162916,0.0005559839,0.0006810783,0.002252946],"category_scores_gemma":[0.003774372,0.0001118177,0.0001451391,0.002763041,0.0127582,0.004366023,0.002043508,0.001193928,0.0001240571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002573811,"about_ca_system_score_gemma":0.00222626,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02346099,"about_ca_topic_score_gemma":0.04098706,"domain_scores_codex":[0.9983387,0.0009031873,0.00008645224,0.000244348,0.0002835176,0.0001438526],"domain_scores_gemma":[0.9975103,0.0009802005,0.0007487065,0.000272851,0.0002989551,0.0001890398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003755046,0.00002609591,0.07808217,0.0003048339,0.00002547305,0.0004178656,0.04712769,0.0007367145,0.001381346,0.7911822,0.02085458,0.05982345],"study_design_scores_gemma":[0.00001533747,0.00005824147,0.3711528,0.001014196,0.00002870153,0.000488757,0.1247189,0.001075306,0.0007754798,0.1689733,0.3316318,0.00006733647],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.4855164,0.01582187,0.02925268,0.1199143,0.0005560305,0.0001899856,0.00117019,0.0001465535,0.3474321],"genre_scores_gemma":[0.9882459,0.002351641,0.003181366,0.002586996,0.00009169786,0.00008096191,0.0001296083,0.00002151341,0.003310316],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.976539,"threshold_uncertainty_score":0.04664886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04969213753279512,"score_gpt":0.2755160439505823,"score_spread":0.2258239064177872,"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."}}