{"id":"W4385270256","doi":"10.1109/icde55515.2023.00333","title":"Modeling Spatial Trajectories with Attribute Representation Learning (Extended Abstract)","year":2023,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Wilfrid Laurier University","funders":"Shandong University; Ministry of Natural Resources","keywords":"Embedding; Computer science; Representation (politics); Trajectory; Context (archaeology); Space (punctuation); Artificial intelligence; Feature learning; Machine learning; Theoretical computer science; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000675583,0.0006637388,0.0005020316,0.0009319698,0.0002585154,0.0007554096,0.001043251,0.0006443479,0.001619452],"category_scores_gemma":[0.003447459,0.0002580955,0.0009413066,0.002448581,0.0003390174,0.001734912,0.0008750507,0.0011715,0.0007268105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006030883,"about_ca_system_score_gemma":0.00052419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01008914,"about_ca_topic_score_gemma":0.008484373,"domain_scores_codex":[0.9996018,0.0001260073,0.00002480903,0.0001540644,0.0000476682,0.00004577225],"domain_scores_gemma":[0.9990477,0.0004509298,0.0001225635,0.0001781897,0.0001547384,0.00004589712],"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":[0.0002009929,0.0002373883,0.01626228,0.0001454453,0.0001632428,0.0001894062,0.0002612938,0.7051708,0.002281598,0.01408502,0.008963005,0.2520396],"study_design_scores_gemma":[0.000004107768,0.00001625621,0.0005389852,0.000005631465,0.000007051977,0.00001943458,0.00002009358,0.9919353,0.0002939845,0.006614692,0.0005397897,0.000004807288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07583004,0.0004872806,0.9184848,0.0005084624,0.00008692322,0.00005430462,0.002301879,0.00144364,0.0008027049],"genre_scores_gemma":[0.8216176,0.0005722409,0.1669929,0.0001403137,0.0001355158,0.0001691147,0.007600423,0.00009988956,0.002671929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01008914,"threshold_uncertainty_score":0.0200609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04824841796132626,"score_gpt":0.3353420178611578,"score_spread":0.2870935998998315,"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."}}