{"id":"W4417303627","doi":"10.1093/jrsssa/qnaf180","title":"Tensor time series change-point detection in cryptocurrency network data","year":2025,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series A (Statistics in Society)","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cryptocurrency; Asset (computer security); Identification (biology); Time series; Tensor (intrinsic definition); Investment (military); Series (stratigraphy)","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":[0.003481275,0.0009392085,0.0006597152,0.002363189,0.0004757718,0.001357298,0.0008729292,0.0009220154,0.001461224],"category_scores_gemma":[0.01755914,0.0002722917,0.0007956427,0.002276126,0.0006716647,0.001519272,0.0009295329,0.001456734,0.0007122766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000947549,"about_ca_system_score_gemma":0.0007067227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01155905,"about_ca_topic_score_gemma":0.009414973,"domain_scores_codex":[0.9982945,0.0006678216,0.0001296109,0.0004461022,0.0003174546,0.0001444809],"domain_scores_gemma":[0.9928387,0.00335536,0.001187568,0.001359053,0.000940066,0.000319261],"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":[0.001680806,0.0004239723,0.1697143,0.0007645367,0.0007393301,0.0008928951,0.0003563869,0.5101982,0.01165959,0.01585292,0.0292651,0.258452],"study_design_scores_gemma":[0.00002488745,0.00005310448,0.01775248,0.00003474367,0.00003799678,0.0001164376,0.00007192035,0.9691723,0.002069829,0.007391667,0.003240256,0.00003437375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6996986,0.00426841,0.2679853,0.002356661,0.0005088446,0.000197467,0.01580636,0.005356482,0.003821995],"genre_scores_gemma":[0.9313846,0.000536788,0.0517036,0.0001200943,0.0001444649,0.00005843716,0.0148385,0.0001386806,0.001074848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01155905,"threshold_uncertainty_score":0.02298349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03837087813339109,"score_gpt":0.3270443071994469,"score_spread":0.2886734290660558,"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."}}