{"id":"W2803548400","doi":"10.1080/10630732.2018.1471874","title":"Automatic Trip Detection with the Dutch Mobile Mobility Panel: Towards Reliable Multiple-Week Trip Registration for Large Samples","year":2018,"lang":"en","type":"article","venue":"Journal of Urban Technology","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Thomas Hospital","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"TRIPS architecture; Duration (music); Destinations; Sample (material); Travel survey; Transport engineering; Travel behavior; Geography; Advertising; Business; Engineering; Tourism","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":[],"consensus_categories":[],"category_scores_codex":[0.003084893,0.0001253162,0.0003169756,0.0002825229,0.0009831015,0.00007326399,0.0004485541,0.0002564581,0.0001387409],"category_scores_gemma":[0.001649062,0.00008452302,0.0001598277,0.0008968558,0.0006861544,0.0002174248,0.00001842448,0.000300499,0.000005986115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002700551,"about_ca_system_score_gemma":0.0004672828,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008093855,"about_ca_topic_score_gemma":0.01837899,"domain_scores_codex":[0.9983585,0.0001837962,0.0005477932,0.0002149676,0.0003724367,0.0003224543],"domain_scores_gemma":[0.9976106,0.0003645531,0.0007010856,0.0003569106,0.0009005338,0.00006626045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002528714,0.005023669,0.081995,0.000494602,0.001475225,0.00002308111,0.04514481,0.0006545094,0.01289945,0.02811618,0.0293932,0.7922516],"study_design_scores_gemma":[0.008019886,0.01532219,0.02737745,0.0002669283,0.001481056,0.00007802065,0.1082184,0.03627402,0.04003894,0.03724357,0.7246068,0.001072683],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9054184,0.0002594292,0.08881642,0.004175509,0.0001968157,0.0007602219,0.00001205941,0.0001467787,0.000214372],"genre_scores_gemma":[0.9976698,0.00002860853,0.001283941,0.00008000778,0.00038606,0.0001051601,0.000002605477,0.00001130309,0.0004325236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7911789,"threshold_uncertainty_score":0.9995331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03248193241551995,"score_gpt":0.2950636966812887,"score_spread":0.2625817642657687,"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."}}