{"id":"W2966871891","doi":"10.1016/j.jglr.2019.07.001","title":"Goals, beneficiaries, and indicators of waterfront revitalization in Great Lakes Areas of Concern and coastal communities","year":2019,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Sustainability; Environmental planning; Environmental resource management; Context (archaeology); Business; Unintended consequences; Redevelopment; Natural capital; Socioeconomic status; Ecosystem services; Geography; Ecosystem; Ecology; Political science; Engineering; Economics; Civil engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001353659,0.00009152343,0.0003519374,0.0002822549,0.00007506054,0.00002587312,0.0001962859,0.00007709255,0.0004268065],"category_scores_gemma":[0.00004508803,0.0000693297,0.00003017167,0.0002761699,0.0005451542,0.0002887968,0.0002040554,0.0003041686,0.000003848277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008269473,"about_ca_system_score_gemma":0.00005827472,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004160696,"about_ca_topic_score_gemma":0.03242794,"domain_scores_codex":[0.9983508,0.00025135,0.0004245841,0.00009726609,0.0006376488,0.0002383538],"domain_scores_gemma":[0.9992338,0.0001859905,0.0002329022,0.0001653392,0.00006911786,0.0001128858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001496252,0.00004572083,0.9928131,0.0001234572,0.00001393776,0.000007058528,0.003190828,0.00004087635,0.0009045693,0.0001212215,0.0003149819,0.002274584],"study_design_scores_gemma":[0.0009371557,0.001020145,0.9894403,0.0004277719,0.00001310832,0.00003429058,0.004683254,0.0001978001,0.0007827537,0.0003073217,0.002058933,0.00009716409],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997252,0.001317301,0.000005828736,0.0002765875,0.00002217335,0.0001784388,0.00002352501,0.000001618393,0.0009224541],"genre_scores_gemma":[0.9972948,0.002204989,0.00005703636,0.0000136703,0.0000166148,0.000001208194,0.000004280098,0.000009187313,0.0003981978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02826724,"threshold_uncertainty_score":0.9852277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04654911905529727,"score_gpt":0.315830908830302,"score_spread":0.2692817897750047,"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."}}