{"id":"W4407222515","doi":"10.3390/geographies5010006","title":"Using the 3-30-300 Indicator to Evaluate Green Space Accessibility and Inequalities: A Case Study of Montreal, Canada","year":2025,"lang":"en","type":"article","venue":"Geographies","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal","funders":"","keywords":"Inequality; Space (punctuation); Mathematics; Computer science; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001312883,0.0006044037,0.0004054951,0.001842804,0.007792111,0.002134542,0.001769392,0.0008378736,0.002450838],"category_scores_gemma":[0.002901476,0.0002972188,0.0006673581,0.004728064,0.001915884,0.0004950261,0.001593612,0.001143939,0.0001838387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07016917,"about_ca_system_score_gemma":0.05769629,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9979094,"about_ca_topic_score_gemma":0.999203,"domain_scores_codex":[0.9984435,0.0003722077,0.00004100511,0.0001458254,0.0003862559,0.0006110942],"domain_scores_gemma":[0.9985752,0.0002058202,0.0001362665,0.00006153674,0.0006346975,0.0003863991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002707407,0.000838445,0.8407661,0.000462367,0.0002545405,0.01120365,0.04507319,0.002974466,0.002311929,0.008029952,0.02382402,0.06399073],"study_design_scores_gemma":[0.00006955583,0.0002998976,0.868418,0.000487874,0.0001773858,0.001517978,0.09463617,0.006480031,0.0007522999,0.0008271243,0.02613552,0.0001981064],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814263,0.001149392,0.0008157998,0.002110079,0.00003408882,0.0003874646,0.002644125,0.00002342649,0.01140942],"genre_scores_gemma":[0.9925844,0.0008840375,0.001778111,0.0004116074,0.000009370209,0.00009968426,0.0005694588,0.00001879575,0.003644496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07016917,"threshold_uncertainty_score":0.5091153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03325556265069794,"score_gpt":0.3127177659859445,"score_spread":0.2794622033352465,"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."}}