{"id":"W2124594716","doi":"10.1002/atr.187","title":"Inferring origin–destination trip matrices from aggregate volumes on groups of links: a case study using volumes inferred from mobile phone data","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Volume (thermodynamics); Consistency (knowledge bases); Aggregate (composite); Data mining; Mobile phone; Matrix (chemical analysis); Aggregate data; Phone; Statistics; Mathematics; Telecommunications; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009034477,0.0001609412,0.000417922,0.0003127512,0.0002997908,0.00004662473,0.0003681684,0.0001203812,0.0001918059],"category_scores_gemma":[0.0002080681,0.0001586146,0.0001224018,0.0005901781,0.0001126011,0.00153918,0.000006781811,0.0003041828,0.000002630966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000129061,"about_ca_system_score_gemma":0.0002678643,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05231214,"about_ca_topic_score_gemma":0.06008119,"domain_scores_codex":[0.9976261,0.000255844,0.001027217,0.000292318,0.0006279288,0.0001705983],"domain_scores_gemma":[0.9973183,0.0003349361,0.001359584,0.000337223,0.0005358193,0.0001141417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008956018,0.002866191,0.4365006,0.00006942776,0.0006829111,0.0007150936,0.3608718,0.07298821,0.00310637,0.00007905006,0.00001156192,0.1212132],"study_design_scores_gemma":[0.006523357,0.001514413,0.5463929,0.0009196802,0.002807436,0.00001168069,0.4163728,0.01735326,0.002234361,0.004648225,0.0003798693,0.0008421049],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905369,0.0003742265,0.008295037,0.00001437062,0.0002778928,0.000334706,0.0001215094,0.00002490326,0.00002043833],"genre_scores_gemma":[0.9939567,0.000165156,0.00547482,0.00001216871,0.0002324727,0.000007670104,0.000126773,0.00001499241,0.000009189323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1203711,"threshold_uncertainty_score":0.9570699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0712092849784739,"score_gpt":0.3462775680263787,"score_spread":0.2750682830479048,"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."}}