{"id":"W2994847231","doi":"10.20377/cgn-81","title":"Building Resilience in Trans-boundary Social-Ecological Systems: Adaptive Governance in the Lake Champlain Richelieu River Basin","year":2019,"lang":"en","type":"article","venue":"Complexity Governance & Networks","topic":"Environmental and Social Impact Assessments","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Resilience (materials science); Corporate governance; Ecological resilience; Adaptive capacity; Structural basin; Adaptive management; Psychological resilience; Environmental resource management; Face (sociological concept); Boundary (topology); Ecological systems theory; Complex adaptive system; Politics; Disturbance (geology); Drainage basin; Geography; Environmental planning; Ecology; Environmental science; Sociology; Political science; Business; Climate change; Geology; Social science; Cartography; Law","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":[0.0006067878,0.00009676284,0.0001116679,0.0008989534,0.002912483,0.00178515,0.0005487658,0.0003559349,0.001071214],"category_scores_gemma":[0.001482549,0.00009079451,0.00009013055,0.0007788,0.00327999,0.0009906173,0.002342953,0.0003315614,0.00003529891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01300147,"about_ca_system_score_gemma":0.007257956,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6474224,"about_ca_topic_score_gemma":0.878612,"domain_scores_codex":[0.9996347,0.0001640385,0.000006893282,0.00003025386,0.00004868941,0.0001153127],"domain_scores_gemma":[0.9994536,0.0001656666,0.0001058627,0.00002004468,0.0001004863,0.0001543839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002098001,0.0002921475,0.4899977,0.0001971469,0.0001571131,0.005535041,0.0883625,0.06457081,0.005424625,0.1927761,0.014084,0.138393],"study_design_scores_gemma":[0.00003988179,0.0001261821,0.7168141,0.0002842803,0.0000594956,0.0005156647,0.1342701,0.05570631,0.001558951,0.04317771,0.04733451,0.000112692],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9772224,0.0001223834,0.001539735,0.003124459,0.000003890078,0.00004294207,0.000052466,0.00002098419,0.01787075],"genre_scores_gemma":[0.9977903,0.00006854075,0.000860839,0.00005838131,0.000001348076,0.00001745969,0.00001581862,0.000001927119,0.001185389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6474224,"threshold_uncertainty_score":0.709308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02153825655203533,"score_gpt":0.2625346474023312,"score_spread":0.2409963908502959,"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."}}