{"id":"W4400727703","doi":"10.1109/mdm61037.2024.00027","title":"Effective Trajectory Imputation using Simple Probabilistic Language Models","year":2024,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Imputation (statistics); Probabilistic logic; Computer science; Trajectory; Simple (philosophy); Language model; Artificial intelligence; Natural language processing; Algorithm; Machine learning; Missing data","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003341806,0.001102863,0.00162325,0.001532393,0.0009272145,0.002316727,0.003189045,0.001481494,0.0037887],"category_scores_gemma":[0.01976373,0.0009088581,0.002010634,0.002907031,0.0008425023,0.005753429,0.002377689,0.002652189,0.002621945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001247712,"about_ca_system_score_gemma":0.002770647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.017626,"about_ca_topic_score_gemma":0.01588301,"domain_scores_codex":[0.9972554,0.001225744,0.0001900648,0.0007428708,0.0003943166,0.000191626],"domain_scores_gemma":[0.9890441,0.008094007,0.000506241,0.001432503,0.000779531,0.0001435823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004125793,0.0001816596,0.006508074,0.0002319519,0.0002180196,0.0003802635,0.0004182574,0.7764853,0.001149054,0.02899265,0.01105916,0.173963],"study_design_scores_gemma":[0.00002177985,0.00001624228,0.000169484,0.00001164621,0.00001291337,0.00003984127,0.00004023908,0.9750503,0.0003272285,0.02333184,0.0009672287,0.00001135602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0228135,0.0003106467,0.9687884,0.0007591965,0.00008671474,0.0000607449,0.001498489,0.004465788,0.001216597],"genre_scores_gemma":[0.5891774,0.0004920294,0.3944105,0.0005151047,0.0001859717,0.0003262632,0.01026362,0.0006152104,0.004013918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.017626,"threshold_uncertainty_score":0.03504676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02410500989287438,"score_gpt":0.3419354468214865,"score_spread":0.3178304369286121,"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."}}