{"id":"W4229560180","doi":"10.1249/00005768-200405001-00368","title":"Exploring Relationships Between Fitness Predictors and Activity Opportunities Using Novel Geographic Information Systems Measurements","year":2004,"lang":"en","type":"article","venue":"Medicine & Science in Sports & Exercise","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Multicollinearity; Range (aeronautics); Statistics; Geographic information system; Descriptive statistics; Linear model; Multivariate statistics; Econometrics; Physical activity; Linear regression; Computer science; Mathematics; Geography; Cartography; Medicine; Engineering","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":[0.001658345,0.0003469613,0.000310697,0.002236121,0.0003869608,0.001067463,0.0004658463,0.0002357507,0.00244514],"category_scores_gemma":[0.01093264,0.0001805659,0.0005749119,0.003668803,0.0002640183,0.0007813229,0.0009012625,0.0004451607,0.0003327648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007674094,"about_ca_system_score_gemma":0.001514128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03276687,"about_ca_topic_score_gemma":0.05034398,"domain_scores_codex":[0.9988077,0.0005099709,0.0001348791,0.0001967398,0.0002739985,0.00007680408],"domain_scores_gemma":[0.9949666,0.002324333,0.001621843,0.0002831642,0.0006263835,0.0001776857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004700447,0.00004739189,0.983853,0.00007267077,0.00009312569,0.00002730563,0.0002903144,0.00134587,0.00013889,0.0003526348,0.0002984891,0.01343331],"study_design_scores_gemma":[0.000007424392,0.0001712111,0.9833891,0.00006842832,0.00006385829,0.00005398423,0.0009786328,0.01300626,0.0002863939,0.000530431,0.001428099,0.00001621831],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9808053,0.0002401798,0.0111492,0.0003272722,0.00001600966,0.0001021092,0.004679802,0.00006840994,0.002611688],"genre_scores_gemma":[0.9922054,0.00009322541,0.006051302,0.0000154428,0.000008320453,0.00007219885,0.001352389,0.000005271248,0.0001963572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03276687,"threshold_uncertainty_score":0.06515229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2801211822659191,"score_gpt":0.3260561208493024,"score_spread":0.04593493858338327,"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."}}