{"id":"W2996460131","doi":"10.3390/ijerph16245046","title":"The Temporal and Spatial Evolution of Marathons in China from 2010 to 2018","year":2019,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Sport and Mega-Event Impacts","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Alberta Health Services","funders":"Jinan University","keywords":"China; Geography; Beijing; Common spatial pattern; Confidence interval; Spatial distribution; Physical geography; RADIUS; Cartography; Demography; Delta; Statistics; Mathematics; Physics; Archaeology; Remote sensing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0003879315,0.0003713049,0.0002240521,0.001751709,0.0004214178,0.0005602762,0.000462375,0.0002098825,0.001301288],"category_scores_gemma":[0.001069889,0.0001749704,0.0003038369,0.002854274,0.0003136894,0.000503696,0.0006196257,0.0002211058,0.0002293665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001374524,"about_ca_system_score_gemma":0.001066149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09948402,"about_ca_topic_score_gemma":0.1508411,"domain_scores_codex":[0.999666,0.00002626588,0.0000294766,0.00008637783,0.00009473685,0.00009715667],"domain_scores_gemma":[0.9991233,0.00005094128,0.0002904938,0.0000417587,0.0003354007,0.0001581808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007064378,0.00001296562,0.9858071,0.00005620795,0.00005026555,0.0002359298,0.001102188,0.0006735685,0.0006394853,0.0002155105,0.001291747,0.009844451],"study_design_scores_gemma":[0.000001067288,0.00001385452,0.9978514,0.00000573748,0.00001073271,0.00004220935,0.0003626801,0.0004615018,0.00005508367,0.00001614303,0.001175698,0.000003965173],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967734,0.000204455,0.0001435133,0.000100257,0.00001134126,0.000008082942,0.001219343,0.00002157043,0.00151806],"genre_scores_gemma":[0.9969918,0.0001818574,0.000105107,0.00001792981,0.00001469511,0.00001332939,0.00167589,0.000004854282,0.0009945402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09948402,"threshold_uncertainty_score":0.1978099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05430389440434314,"score_gpt":0.3977885685644249,"score_spread":0.3434846741600818,"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."}}