{"id":"W2626890125","doi":"10.1140/epjds/s13688-017-0108-6","title":"Inferring social influence in transport mode choice using mobile phone data","year":2017,"lang":"en","type":"article","venue":"EPJ Data Science","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Thailand Research Fund","keywords":"Interpersonal ties; Mode (computer interface); Public transport; Mobile phone; Mode choice; Phone; Social network (sociolinguistics); Computer science; Social psychology; Psychology; Telecommunications; Transport engineering; Engineering; Social media; Human–computer interaction; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","open_science"],"consensus_categories":["sts"],"category_scores_codex":[0.004866041,0.0001113881,0.0001902029,0.000156559,0.00460461,0.0005836264,0.008683933,0.00006689504,0.0001280968],"category_scores_gemma":[0.002184317,0.0001200937,0.00002269358,0.0007317635,0.002913506,0.008630564,0.0009893563,0.0002095892,0.0000291789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001806438,"about_ca_system_score_gemma":0.001557758,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2103482,"about_ca_topic_score_gemma":0.2802511,"domain_scores_codex":[0.9972956,0.00009082769,0.000304457,0.0009150562,0.0008857407,0.0005083527],"domain_scores_gemma":[0.9963869,0.0001064606,0.0001941893,0.00302741,0.0001399231,0.0001451391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001867349,0.0004484341,0.881671,0.00006219566,0.00002555055,0.00002411631,0.03737064,0.01464706,0.01019607,0.003144572,0.0001981181,0.05219354],"study_design_scores_gemma":[0.000559528,0.00001390156,0.7101102,0.0001073799,0.00006868332,7.223967e-7,0.004594128,0.2617793,0.0002686597,0.0006915543,0.02111284,0.0006931302],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955595,0.00003062154,0.001532638,0.0003684685,0.00013273,0.0001922014,0.0003168501,0.00003549784,0.001831559],"genre_scores_gemma":[0.9988574,0.00002991413,0.0006248282,0.00008883572,0.0002144131,0.000007068315,0.000109429,0.000005497853,0.00006258215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2471322,"threshold_uncertainty_score":0.9998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1959329568068157,"score_gpt":0.4765131416105525,"score_spread":0.2805801848037368,"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."}}