{"id":"W3197170627","doi":"10.1016/j.ufug.2021.127335","title":"Benches, fountains and trees: Using mixed-methods with questionnaire and smartphone data to design urban green spaces","year":2021,"lang":"en","type":"article","venue":"Urban forestry & urban greening","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Space (punctuation); Perception; Computer science; Point (geometry); Tracking (education); Data collection; Psychology; Applied psychology; Human–computer interaction; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001047062,0.000565294,0.0005915555,0.0001451499,0.000733949,0.0002702249,0.0005677984,0.000228892,0.0001523096],"category_scores_gemma":[0.0001467824,0.0005272905,0.00005118965,0.0008383599,0.0005188406,0.000997351,0.001267067,0.0004693446,0.00002869502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002549757,"about_ca_system_score_gemma":0.0001775247,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.009068757,"about_ca_topic_score_gemma":0.01792179,"domain_scores_codex":[0.9960185,0.0004324993,0.0004851987,0.001529379,0.0005771055,0.0009572763],"domain_scores_gemma":[0.997367,0.0002323544,0.0002406345,0.001325379,0.00004414115,0.0007904601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001026525,0.00005982869,0.9673688,0.00006051509,0.00006131492,0.0001587441,0.002032816,0.0002865589,0.00162619,0.00009082963,0.0176293,0.0105224],"study_design_scores_gemma":[0.001510137,0.0008432512,0.943768,0.0007189847,0.0003390297,0.0005585255,0.001896957,0.02729794,0.0005528922,0.0003441073,0.02071825,0.001451911],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8911552,0.003986727,0.1022712,0.001183035,0.0002279295,0.0006794888,0.00006801026,0.0001715757,0.0002568089],"genre_scores_gemma":[0.7137787,0.0000658596,0.2823835,0.0006300849,0.0004026996,0.00002491819,0.00008498033,0.000111532,0.002517773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1801123,"threshold_uncertainty_score":0.9999986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05499389196821629,"score_gpt":0.3095135848883821,"score_spread":0.2545196929201658,"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."}}