{"id":"W6936652876","doi":"10.58079/v811","title":"Constructing connections: urban forestry and Toronto’s West Don Lands revitalization","year":2010,"lang":"en","type":"article","venue":"OpenEdition (OpenEdition)","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Urban forestry; Community forestry; Sustainability; Vegetation (pathology); Government (linguistics)","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":[0.0009734974,0.0002867021,0.0001853455,0.0007003603,0.02498628,0.007965136,0.001103424,0.002436904,0.01895005],"category_scores_gemma":[0.00193746,0.0002010007,0.0002199475,0.001602065,0.01205629,0.002479032,0.005214768,0.003368945,0.0004454979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05972621,"about_ca_system_score_gemma":0.04147848,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8913413,"about_ca_topic_score_gemma":0.9857656,"domain_scores_codex":[0.9985134,0.0002654279,0.00001818464,0.00009576049,0.0001800225,0.0009272579],"domain_scores_gemma":[0.9987538,0.0001747696,0.00008117618,0.00006764146,0.0001592841,0.0007632377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001423245,0.00007842746,0.01600411,0.00012846,0.00002322142,0.002380884,0.2863367,0.001101286,0.0008151399,0.4940005,0.1384535,0.06053545],"study_design_scores_gemma":[0.00002141692,0.00003567126,0.03598617,0.0001144507,0.00001541606,0.0001645843,0.302151,0.0003186895,0.000409706,0.01141271,0.6493301,0.00004006148],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4599558,0.003226502,0.0007154595,0.06960522,0.0005730367,0.00007681058,0.0002492405,0.00006093719,0.465537],"genre_scores_gemma":[0.9342899,0.0004862752,0.0002251094,0.001162187,0.00004649455,0.00001676179,0.00004333429,0.00002449357,0.06370552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1086587,"threshold_uncertainty_score":0.433346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01094214717222002,"score_gpt":0.2391934342445143,"score_spread":0.2282512870722943,"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."}}