{"id":"W4413836429","doi":"10.1080/14927713.2025.2551525","title":"Who comes to the park and why? A cluster analysis based on serious leisure framework","year":2025,"lang":"en","type":"article","venue":"Leisure/Loisir","topic":"Recreation, Leisure, Wilderness Management","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Cluster (spacecraft); Geography; Regional science; Economic geography; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.002334177,0.0003961044,0.000394368,0.002998345,0.003122502,0.003694332,0.0008620256,0.0003214258,0.002190077],"category_scores_gemma":[0.003441018,0.0001366899,0.0007645411,0.003308227,0.002209023,0.001080714,0.002212299,0.0007782966,0.0001418263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003715469,"about_ca_system_score_gemma":0.005852074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1212401,"about_ca_topic_score_gemma":0.1460421,"domain_scores_codex":[0.9985607,0.0006225434,0.00007419596,0.000263059,0.0002226203,0.0002569131],"domain_scores_gemma":[0.998867,0.0003972456,0.0001583041,0.00007090136,0.00031755,0.0001890209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004435625,0.0003996876,0.3918634,0.0005631926,0.0002821844,0.001564888,0.3046014,0.003230085,0.003934534,0.09158084,0.01233177,0.1892045],"study_design_scores_gemma":[0.00004505605,0.0002636199,0.3308903,0.0004499936,0.0002489061,0.0007787797,0.5426056,0.03873787,0.001340465,0.05255919,0.03193871,0.0001416437],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8781743,0.0005121238,0.09942637,0.004160951,0.00006881512,0.001472426,0.0009722592,0.0001368089,0.01507592],"genre_scores_gemma":[0.954518,0.0001886044,0.0422671,0.0001015147,0.00001007072,0.0002965534,0.0004452732,0.00001953185,0.002153171],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1212401,"threshold_uncertainty_score":0.2410688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01205610286833088,"score_gpt":0.3053765675320728,"score_spread":0.293320464663742,"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."}}