{"id":"W3128993782","doi":"10.1007/s11042-020-10492-6","title":"Deep learning based origin-destination prediction via contextual information fusion","year":2021,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Novelis (Canada)","funders":"National Key Research and Development Program of China","keywords":"Computer science; Inference; Artificial intelligence; Context (archaeology); Train; Deep learning; Machine learning; Task (project management); Convolutional neural network; Urban computing; Contextual design; Data mining","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":[0.000449388,0.00008045576,0.0001011856,0.00007820216,0.001094996,0.0001888971,0.00007164425,0.00009662649,0.0003273391],"category_scores_gemma":[0.0004454878,0.00008794475,0.00004572054,0.0004362267,0.0001493261,0.0005065043,0.0000156102,0.0001395147,0.0000926138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008628533,"about_ca_system_score_gemma":0.0001670724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006504886,"about_ca_topic_score_gemma":0.00145458,"domain_scores_codex":[0.999009,0.0001462252,0.0002518354,0.000182858,0.0002607135,0.0001494308],"domain_scores_gemma":[0.9988953,0.0003524387,0.0001078643,0.0001367782,0.0003945626,0.000113037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005525916,0.00009711363,0.006259571,0.00002227596,0.00001186379,2.692463e-7,0.003817383,0.003445076,0.0005120001,0.004332307,0.00006905251,0.9814276],"study_design_scores_gemma":[0.0006625744,0.00002882243,0.03690644,0.00002282537,0.00008032356,7.89501e-7,0.00586958,0.5478053,0.0003637214,0.0008016019,0.4072343,0.0002237309],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01806613,0.0001246883,0.9737728,0.001653337,0.00007042395,0.0005892783,0.00003494332,0.0002061717,0.005482189],"genre_scores_gemma":[0.995712,0.0001007068,0.00194416,0.0001972523,0.0002244922,0.0003285913,0.001190293,0.000005058303,0.0002973772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9812039,"threshold_uncertainty_score":0.8421936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01954394972105072,"score_gpt":0.2805702692077245,"score_spread":0.2610263194866738,"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."}}