{"id":"W2990424650","doi":"10.3390/su11236735","title":"Institutional Innovation for Nature-Based Coastal Adaptation: Lessons from Salt Marsh Restoration in Nova Scotia, Canada","year":2019,"lang":"en","type":"article","venue":"Sustainability","topic":"Coastal wetland ecosystem dynamics","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University; Dalhousie University","funders":"Natural Resources Canada; Government of Canada","keywords":"Adaptation (eye); Autonomy; Nova scotia; Business; Adaptive capacity; Climate change; Public relations; Political science; Environmental planning; Knowledge management; Environmental resource management; Sociology; Computer science; Geography; Economics; Ecology; Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002246175,0.0002346261,0.0002640918,0.0009686633,0.01226537,0.004495643,0.001403481,0.000652278,0.002656489],"category_scores_gemma":[0.003946053,0.0001772001,0.0002334982,0.001361389,0.007631524,0.001077761,0.00335983,0.001198865,0.0001336902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1265432,"about_ca_system_score_gemma":0.1861592,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9916918,"about_ca_topic_score_gemma":0.9979391,"domain_scores_codex":[0.9982209,0.0004671832,0.00004766422,0.0001403335,0.0003035942,0.0008202964],"domain_scores_gemma":[0.9963295,0.0008633015,0.0002883915,0.0001920143,0.001097566,0.001229226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002591759,0.0004872068,0.249378,0.0006868758,0.0001058342,0.01068872,0.5029082,0.004852257,0.002925816,0.0728428,0.01897813,0.1358869],"study_design_scores_gemma":[0.00003810514,0.0001093786,0.1900385,0.0005977763,0.0000577331,0.0003841168,0.6836443,0.002155623,0.0008277876,0.004066066,0.1180041,0.00007656481],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9381394,0.0009817476,0.001033727,0.01065527,0.00006105403,0.0001861408,0.0002180355,0.0000213776,0.04870327],"genre_scores_gemma":[0.9921253,0.000609441,0.00068558,0.000317983,0.000003885616,0.00002149096,0.0000622414,0.000007900061,0.006166149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1265432,"threshold_uncertainty_score":0.9181395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00890407429473585,"score_gpt":0.2448414893294783,"score_spread":0.2359374150347425,"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."}}