{"id":"W4407953229","doi":"10.1145/3701551.3703521","title":"Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks","year":2025,"lang":"en","type":"article","venue":"","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Temporality; Random walk; Scalability; Metropolis–Hastings algorithm; Computer science; Blockchain; Data science; Artificial intelligence; Mathematics; Markov chain Monte Carlo; Epistemology; Statistics; Database; Computer security","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":[0.002656113,0.001162922,0.001609651,0.001110799,0.0007127064,0.001413828,0.003104482,0.001418569,0.003033809],"category_scores_gemma":[0.01189649,0.0008836498,0.001021505,0.00109183,0.0009899155,0.003600521,0.001847174,0.002169611,0.0005128136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00131863,"about_ca_system_score_gemma":0.002488307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009997672,"about_ca_topic_score_gemma":0.01634845,"domain_scores_codex":[0.9990489,0.0004118762,0.00004581399,0.0002009986,0.0001801317,0.0001123016],"domain_scores_gemma":[0.9930053,0.005307816,0.0003686082,0.0005842714,0.0004829449,0.0002509821],"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":[0.0001099946,0.000108435,0.002207183,0.0001059752,0.00006804716,0.00009640989,0.00006459704,0.9376648,0.001301215,0.0163032,0.001445617,0.04052445],"study_design_scores_gemma":[0.000004962268,0.000006375562,0.00002797516,0.000001381512,0.000002754064,0.000003576029,0.00000305734,0.9960884,0.0001133675,0.003670304,0.0000762861,0.000001570454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07169479,0.0005601252,0.9226023,0.0008482161,0.00006710325,0.0001379044,0.0001894704,0.001773529,0.002126578],"genre_scores_gemma":[0.6809573,0.0003343904,0.3142281,0.0002679224,0.0001078184,0.000274577,0.0006395911,0.0004252424,0.002765099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009997672,"threshold_uncertainty_score":0.01987892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008012203288546849,"score_gpt":0.2398847482021589,"score_spread":0.231872544913612,"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."}}