{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004883817,0.001142237,0.001178155,0.001456788,0.000477318,0.0009156317,0.001593877,0.001208657,0.002009997],"category_scores_gemma":[0.001559212,0.0004514513,0.0009219276,0.001846104,0.0003716768,0.001378395,0.001296541,0.001613087,0.001388741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007340609,"about_ca_system_score_gemma":0.001117506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02189145,"about_ca_topic_score_gemma":0.02234369,"domain_scores_codex":[0.9996258,0.00004689536,0.00001852048,0.0001421668,0.00005874265,0.0001079058],"domain_scores_gemma":[0.9995201,0.0001816876,0.00005111888,0.00006228976,0.0001434014,0.00004142956],"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":[0.0005606918,0.0006941934,0.02344992,0.0001265327,0.0002780657,0.0003958646,0.0001455737,0.5242404,0.005968149,0.006344126,0.01364312,0.4241534],"study_design_scores_gemma":[0.000003963046,0.00001569611,0.0006332637,0.000007335846,0.00001705837,0.00001796233,0.00001235293,0.9963122,0.000852703,0.001796151,0.0003258172,0.000005490193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1937734,0.002028151,0.7915764,0.001000644,0.0005416663,0.00006797994,0.002729542,0.003429667,0.004852599],"genre_scores_gemma":[0.9470568,0.0004637835,0.04558877,0.0001596907,0.0001508412,0.00003590007,0.002584337,0.00005941871,0.003900388],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02189145,"threshold_uncertainty_score":0.04352802,"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."}}