{"id":"W4411120684","doi":"10.1609/icwsm.v19i1.35955","title":"Mobility Networked Time-Series Forecasting Benchmark Datasets","year":2025,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Benchmark (surveying); Series (stratigraphy); Time series; Computer science; Artificial intelligence; Machine learning; Data mining; Geography; Cartography; Geology","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":[0.001304714,0.001412736,0.0006104224,0.001740046,0.0007395367,0.0007660683,0.002586712,0.001612137,0.003424367],"category_scores_gemma":[0.005356915,0.000225748,0.0009657991,0.002899735,0.0003675841,0.001112708,0.0009916291,0.00142988,0.002052765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001211064,"about_ca_system_score_gemma":0.00106756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02400286,"about_ca_topic_score_gemma":0.03098188,"domain_scores_codex":[0.9992577,0.000153414,0.0001156557,0.0002088694,0.0001702755,0.00009407365],"domain_scores_gemma":[0.9982336,0.000478903,0.0002203701,0.0004177535,0.0004884109,0.0001609451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008984462,0.001458067,0.04820524,0.001234656,0.0004460761,0.0008087289,0.0002465404,0.2345905,0.002369056,0.00943951,0.6031607,0.09714241],"study_design_scores_gemma":[0.0004313451,0.0004454895,0.07361226,0.0002591982,0.0001437546,0.0005924901,0.000696987,0.7298002,0.004997219,0.009801867,0.1790517,0.0001674882],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2342815,0.001952538,0.02067517,0.003356668,0.001039395,0.0006917838,0.7224218,0.005837035,0.009744046],"genre_scores_gemma":[0.2038588,0.0008264226,0.02103195,0.0002730433,0.0002027856,0.0006274488,0.7700444,0.0001387145,0.002996407],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02400286,"threshold_uncertainty_score":0.04772627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03104714303114981,"score_gpt":0.2842834470880672,"score_spread":0.2532363040569174,"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."}}