{"id":"W2907765766","doi":"10.1289/isee.2014.s-054","title":"Greenness and Health: Using Linked Data to Disentangle Effects from Spatially Crrelated Built Environment Factors","year":2014,"lang":"en","type":"article","venue":"ISEE Conference Abstracts","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Interquartile range; Environmental health; Medicine; Normalized Difference Vegetation Index; Demography; Cohort; Built environment; Gerontology; Population; Geography; Climate change; Ecology","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":[0.009192847,0.000653325,0.0007479617,0.005327973,0.0006234474,0.003556309,0.001035471,0.001160631,0.001628479],"category_scores_gemma":[0.02772838,0.0005664819,0.001916403,0.009198321,0.0006147505,0.001482727,0.003040397,0.001372469,0.0002865127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001182002,"about_ca_system_score_gemma":0.001426242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07736237,"about_ca_topic_score_gemma":0.09245871,"domain_scores_codex":[0.9924001,0.004806393,0.0005169267,0.001040958,0.0009023508,0.0003332495],"domain_scores_gemma":[0.9645768,0.02015024,0.007440056,0.004307663,0.002589397,0.0009358658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009782595,0.00004886759,0.9842929,0.00009189196,0.001203389,0.00005989049,0.000165697,0.001691884,0.000135825,0.0004116311,0.0009108791,0.01088936],"study_design_scores_gemma":[0.0000506161,0.0001036019,0.9701037,0.0002857254,0.0008048214,0.00009808949,0.0007832393,0.01867142,0.0002990239,0.003700705,0.005018225,0.00008083347],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9246321,0.007781364,0.0212196,0.003480503,0.0002439838,0.0002705673,0.03752328,0.0001742724,0.004674233],"genre_scores_gemma":[0.9682649,0.001693733,0.01547925,0.000465089,0.0002223453,0.0003224777,0.01266513,0.00005644311,0.0008306477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07736237,"threshold_uncertainty_score":0.1538241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1139731854712856,"score_gpt":0.3076534714322821,"score_spread":0.1936802859609964,"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."}}