{"id":"W6996960999","doi":"","title":"Towards Effective Watershed Governance: A Case Study of the Grand River Basin","year":2022,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Sustainability and Climate Change Governance","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Watershed; Corporate governance; Watershed management; Variety (cybernetics); Drainage basin","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":[0.002157126,0.0002890322,0.0002686454,0.0008972658,0.02052706,0.004391377,0.001767407,0.002458785,0.002377112],"category_scores_gemma":[0.003248532,0.0002927578,0.0002700983,0.002499991,0.008950741,0.001705434,0.003512022,0.002400364,0.0001506935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03109677,"about_ca_system_score_gemma":0.03638884,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8064004,"about_ca_topic_score_gemma":0.9578299,"domain_scores_codex":[0.9965311,0.001803061,0.00005486691,0.0002005058,0.0005677772,0.0008428328],"domain_scores_gemma":[0.9978491,0.0008104006,0.000127492,0.00009432652,0.0003848748,0.0007337415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005208607,0.0004474123,0.02702205,0.0002399643,0.00002181257,0.03334227,0.8238043,0.002710052,0.002576909,0.06560618,0.01001221,0.03416475],"study_design_scores_gemma":[0.00001190259,0.00007205466,0.01218792,0.0001337196,0.00001077193,0.001214475,0.9051585,0.001253939,0.0005696355,0.002310715,0.07705237,0.00002410612],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.941072,0.0002673912,0.002570405,0.00706632,0.00002975513,0.0002596586,0.00007318661,0.00002253688,0.04863876],"genre_scores_gemma":[0.9821729,0.0006844091,0.004769715,0.0008878838,0.000008767662,0.00008709695,0.00004979783,0.00001969678,0.01131975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1935996,"threshold_uncertainty_score":0.3894794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00755984923845015,"score_gpt":0.2067211118586283,"score_spread":0.1991612626201782,"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."}}