{"id":"W3081213223","doi":"10.1007/s11116-020-10135-7","title":"Are we there yet? Assessing smartphone apps as full-fledged tools for activity-travel surveys","year":2020,"lang":"en","type":"article","venue":"Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Ministry of Transportation of Ontario","funders":"","keywords":"Smartphone app; Data collection; Computer science; Ground truth; Travel survey; Suite; Logging; TRACE (psycholinguistics); Inference; Smartphone application; Data quality; Travel behavior; Transport engineering; Engineering; Human–computer interaction; Machine learning; Artificial intelligence; Multimedia; Geography; Statistics; Operations management","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02990386,0.0008978696,0.0009150081,0.00132144,0.0009656362,0.007279515,0.001452621,0.002529776,0.003261645],"category_scores_gemma":[0.1323849,0.0006784206,0.001088007,0.001579687,0.0009188232,0.01292679,0.002767158,0.001667625,0.001498908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007529255,"about_ca_system_score_gemma":0.002981143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005522075,"about_ca_topic_score_gemma":0.01614637,"domain_scores_codex":[0.982981,0.009132492,0.001728455,0.001230774,0.00380959,0.001117884],"domain_scores_gemma":[0.8967077,0.06002906,0.01073998,0.002980024,0.02570453,0.003838795],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000477912,0.0006109727,0.6697759,0.001800547,0.0006087782,0.000128332,0.01337403,0.0003917103,0.00111625,0.001617386,0.004669229,0.3054289],"study_design_scores_gemma":[0.00009785646,0.004926683,0.7617518,0.007717052,0.002560363,0.0007887517,0.1496232,0.006057309,0.00298063,0.007283381,0.05584573,0.0003673453],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9497753,0.009414224,0.01378495,0.0121786,0.0007722306,0.0006138689,0.001303505,0.0002072854,0.01195016],"genre_scores_gemma":[0.9684766,0.003650327,0.0230578,0.002082888,0.0001608682,0.0005215361,0.0004849631,0.00004116825,0.001523962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9700961,"threshold_uncertainty_score":0.1581486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1151171388082643,"score_gpt":0.3553717844682,"score_spread":0.2402546456599357,"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."}}