{"id":"W2582090204","doi":"10.1186/s12942-017-0077-9","title":"International comparison of observation-specific spatial buffers: maximizing the ability to estimate physical activity","year":2017,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Health Research Council of New Zealand; National Health and Medical Research Council; National Heart, Lung, and Blood Institute; Medical Research Council; National Cancer Institute; National Institutes of Health","keywords":"Health geography; Health informatics; Physical activity; Public health; Human geography; Computer science; Statistics; Econometrics; Environmental health; Geography; Medicine; Mathematics; Health policy; International health; Physical medicine and rehabilitation; Economic geography","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":[0.01543918,0.0007369508,0.0004826491,0.001955171,0.0003402217,0.001311672,0.001067912,0.0004490256,0.001580819],"category_scores_gemma":[0.04531165,0.0003106019,0.0009473491,0.002747512,0.0005957281,0.001146122,0.002425696,0.0004300068,0.0002554281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004851151,"about_ca_system_score_gemma":0.0006863853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006554052,"about_ca_topic_score_gemma":0.01075928,"domain_scores_codex":[0.9929293,0.004884019,0.0005088256,0.0009099394,0.0006109787,0.0001568673],"domain_scores_gemma":[0.9796486,0.009713563,0.005187995,0.00290603,0.002252519,0.0002912427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007028464,0.0001137729,0.9177234,0.0003302325,0.0009194086,0.00004462903,0.001408585,0.01105968,0.0010048,0.001951193,0.001153259,0.06358814],"study_design_scores_gemma":[0.0001144492,0.0008708991,0.9351751,0.0003263004,0.000735984,0.0001642624,0.002789882,0.04531404,0.003698031,0.00337446,0.007345367,0.00009122075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8815603,0.0006878236,0.1088461,0.0001599646,0.00006342492,0.000455799,0.002785292,0.0001593944,0.005281807],"genre_scores_gemma":[0.9417307,0.0002283355,0.05470286,0.00003281129,0.00001528474,0.0005195658,0.002342802,0.00003388837,0.000393857],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01543918,"threshold_uncertainty_score":0.08165115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1116727724270437,"score_gpt":0.4628207439944978,"score_spread":0.3511479715674541,"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."}}