{"id":"W2555726166","doi":"10.2196/mhealth.5771","title":"Benefits of Mobile Phone Technology for Personal Environmental Monitoring","year":2016,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Environmental Health Sciences; National Institutes of Health","keywords":"Geolocation; Global Positioning System; Mobile phone; Computer science; Mobile phone tracking; Phone; Matching (statistics); Activity tracker; Tracking (education); Mobile technology; Mobile computing; Geography; Statistics; Telecommunications; Psychology; GSM services; Mathematics; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00188405,0.0007548975,0.0004099583,0.001215693,0.0002781365,0.001691065,0.0006745109,0.001086751,0.005713173],"category_scores_gemma":[0.007483205,0.0002669147,0.000517783,0.001102337,0.0002989427,0.001080138,0.0008816103,0.0007374419,0.00210587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003647016,"about_ca_system_score_gemma":0.0003782153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00187491,"about_ca_topic_score_gemma":0.002243038,"domain_scores_codex":[0.9980417,0.0008377916,0.0001098143,0.0003503353,0.000596883,0.00006334187],"domain_scores_gemma":[0.9924324,0.003783135,0.0009734201,0.0006111047,0.001980166,0.0002198806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008425963,0.0003205966,0.1958593,0.0009691461,0.000280218,0.0003829005,0.0004690511,0.002895949,0.01530455,0.002278653,0.01833852,0.7620586],"study_design_scores_gemma":[0.0003398729,0.004147178,0.6187398,0.002011362,0.001230503,0.008129659,0.001598334,0.02956183,0.02732412,0.01157023,0.2950022,0.0003448876],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5008042,0.08182244,0.2311845,0.03237826,0.004349014,0.0012866,0.0141299,0.005467344,0.1285778],"genre_scores_gemma":[0.8141347,0.01095015,0.15694,0.003344058,0.00210142,0.0005932427,0.002052049,0.0001912307,0.009693319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005713173,"threshold_uncertainty_score":0.01911247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04149440358514885,"score_gpt":0.3596504198381498,"score_spread":0.3181560162530009,"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."}}