{"id":"W1821450441","doi":"","title":"Learning Latent Factor Models of Human Travel","year":2012,"lang":"en","type":"preprint","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Factor (programming language); Computer science; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008246624,0.0001307849,0.0003183974,0.0001088551,0.000377765,0.00004826197,0.0003258543,0.0002836778,0.004063854],"category_scores_gemma":[0.00006174428,0.0001244019,0.0002704787,0.0001039997,0.0002116883,0.00008764636,0.0001119497,0.0004285113,0.0000294534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001148945,"about_ca_system_score_gemma":0.0002251221,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05342692,"about_ca_topic_score_gemma":0.01345232,"domain_scores_codex":[0.9984259,0.0003129394,0.0003381866,0.0002559202,0.0004239391,0.0002431046],"domain_scores_gemma":[0.9991416,0.00008603316,0.0002117253,0.0002584246,0.0001784179,0.0001237581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000149701,0.001124157,0.058768,0.0007132291,0.0008001587,0.000001273979,0.4688155,0.1144519,0.001734182,0.3097396,0.0003308672,0.04350607],"study_design_scores_gemma":[0.002049208,0.0004748294,0.2237022,0.001302365,0.002875655,3.597904e-7,0.1627157,0.1672455,0.007908799,0.4173894,0.00735933,0.006976559],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8354487,0.0002261918,0.02649402,0.0004200966,0.0002215578,0.0004631185,0.00002128314,0.0001440777,0.136561],"genre_scores_gemma":[0.98946,0.00005180195,0.00009826799,0.00002045703,0.0001894913,0.00001586624,0.00004306163,0.000009348423,0.0101117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3060998,"threshold_uncertainty_score":0.9968466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0973565108744705,"score_gpt":0.3452355793040515,"score_spread":0.247879068429581,"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."}}