{"id":"W4366309494","doi":"10.1109/access.2023.3268030","title":"Embedding and Trajectories of Temporal Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Japan Society for the Promotion of Science; Canada Research Chairs; Air Force Office of Scientific Research; Japan Science and Technology Agency; Sumitomo Foundation; National Science Foundation","keywords":"Embedding; Computer science; Temporal database; Trajectory; Scaling; Node (physics); Theoretical computer science; Sequence (biology); Artificial intelligence; Data mining; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000109332,0.00008008149,0.0001717051,0.00007557375,0.00005681201,0.00005734965,0.0001743251,0.00001912877,0.0000737952],"category_scores_gemma":[0.00000144158,0.0000753546,0.00005447216,0.0004379843,0.00004118001,0.0001536765,0.00007978798,0.00006679763,0.000001674617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003686216,"about_ca_system_score_gemma":0.000008670628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003481284,"about_ca_topic_score_gemma":0.000015121,"domain_scores_codex":[0.9994673,0.00001927636,0.0001571748,0.0001340784,0.0000769415,0.0001452607],"domain_scores_gemma":[0.9996481,0.00006463322,0.00007903742,0.0001463428,0.00003126845,0.00003060061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000106729,0.00003028915,0.9413632,0.00001849199,0.0001351067,0.000002082034,0.0001906016,0.01237366,0.0003571154,0.003617709,0.01360041,0.02830063],"study_design_scores_gemma":[0.0008116758,0.00009438198,0.1524335,0.0001867405,0.0002232193,0.000001169928,0.0005584909,0.7545333,0.02042368,0.06347308,0.006322394,0.0009383616],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9491696,0.00004209937,0.04911064,0.000026305,0.0001071274,0.00007930287,0.000005619927,0.0001196663,0.001339663],"genre_scores_gemma":[0.9993089,0.000007349305,0.0002236392,0.00000699323,0.0003080128,0.00001387662,0.00001704258,0.00001068399,0.0001035393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7889297,"threshold_uncertainty_score":0.3072872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02637981770346357,"score_gpt":0.3383292193140964,"score_spread":0.3119494016106328,"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."}}