{"id":"W3045809617","doi":"10.1177/2514848620943890","title":"Watershed or bank-to-bank? Scales of governance and the geographic definition of Great Lakes Areas of Concern","year":2020,"lang":"en","type":"article","venue":"Environment and Planning E Nature and Space","topic":"Water Governance and Infrastructure","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Watershed; Corporate governance; Scale (ratio); Stakeholder; Geography; Environmental resource management; Scholarship; Watershed management; Environmental planning; Business; Political science; Economics; Public relations; Finance","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001325088,0.00008330679,0.0001959049,0.00001224226,0.00008784404,0.00001242507,0.00007127829,0.0001101014,0.00002797798],"category_scores_gemma":[0.00005422191,0.0000494305,0.00002596565,0.00006153536,0.0004822241,0.000075357,0.00003640687,0.0001300489,2.345001e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003771992,"about_ca_system_score_gemma":0.000007117162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001763296,"about_ca_topic_score_gemma":0.00005444905,"domain_scores_codex":[0.9994047,0.00004734992,0.0001132866,0.0001350237,0.0002000672,0.0000995634],"domain_scores_gemma":[0.9996542,0.00009862937,0.0001299147,0.00005730288,0.000008435683,0.00005154489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001257369,0.00001780332,0.9199944,0.0001726417,0.00008842309,0.000007613307,0.05629844,0.00005921976,0.002858054,0.01446394,0.00297157,0.001810583],"study_design_scores_gemma":[0.001668334,0.0002970711,0.9491265,0.0002370598,0.00008927641,0.000002650539,0.004963423,0.00004088385,0.003841024,0.002041113,0.0374754,0.0002172079],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912543,0.004582591,0.00001864527,0.003152191,0.00002791731,0.0001230767,0.00005021477,0.000004105837,0.0007870171],"genre_scores_gemma":[0.9961896,0.003219724,0.0001883331,0.0002308929,0.00005728393,0.000001650392,0.000004403169,0.000003689567,0.0001044438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05133501,"threshold_uncertainty_score":0.2015717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01334710099156429,"score_gpt":0.2228996847018001,"score_spread":0.2095525837102358,"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."}}