{"id":"W1232129636","doi":"10.1007/s11116-015-9643-9","title":"Social interactions in transportation: analyzing groups and spatial networks","year":2015,"lang":"en","type":"article","venue":"Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Universität für Bodenkultur Wien; Deutsche Forschungsgemeinschaft","keywords":"TRIPS architecture; Travel behavior; Work (physics); Demand management; Context (archaeology); Mode choice; Transport engineering; Economics; Regional science; Operations research; Marketing; Sociology; Business; Microeconomics; Public transport; Geography; Engineering","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":[0.000383247,0.0001075119,0.0001476336,0.0001570261,0.0002618253,0.0000492407,0.00006129573,0.00008548312,0.00004502943],"category_scores_gemma":[0.00001512034,0.0001285641,0.00004030075,0.0004588761,0.00009454526,0.0005119469,2.952532e-7,0.0001603092,0.000003161702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006180567,"about_ca_system_score_gemma":0.00009345762,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008471164,"about_ca_topic_score_gemma":0.2114215,"domain_scores_codex":[0.9988902,0.00008435318,0.0003469134,0.0002154477,0.0002527057,0.0002104323],"domain_scores_gemma":[0.9995539,0.00005017095,0.0001080637,0.00004630116,0.0001193971,0.0001221937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002008447,0.0001040161,0.7077819,0.00001971451,0.00002411544,0.00002744843,0.1772601,0.07752151,0.00001551136,0.02542937,0.0003459677,0.01126951],"study_design_scores_gemma":[0.001008364,0.00002250336,0.98119,0.00002923451,0.00005092443,2.328376e-7,0.008499576,0.004683718,0.000006587928,0.0008311128,0.003465204,0.0002125803],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8496017,0.0001152687,0.1463281,0.00136654,0.0003609023,0.0002869098,0.00002741605,0.0001775168,0.001735722],"genre_scores_gemma":[0.9982753,0.00005794899,0.0005649461,0.00005899591,0.0002001108,0.00002265413,0.0006873668,0.00001296114,0.0001196818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.273408,"threshold_uncertainty_score":0.9981315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03775907459611044,"score_gpt":0.3195234263295202,"score_spread":0.2817643517334097,"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."}}