{"id":"W4320729805","doi":"10.54097/hset.v31i.5152","title":"Application of Random Walks in Data Processing","year":2023,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Random walk; Random field; Computer science; Field (mathematics); Random walker algorithm; Markov process; Stochastic process; Markov chain; Algorithm; Statistical physics; Theoretical computer science; Mathematics; Artificial intelligence; Statistics; Machine learning; Physics","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":[0.001676784,0.0009347938,0.001322071,0.002588222,0.0006263992,0.002156089,0.001107759,0.001801134,0.00297583],"category_scores_gemma":[0.006698433,0.0004656141,0.001311066,0.004162009,0.00145684,0.002742784,0.001569909,0.00206705,0.001598281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008305803,"about_ca_system_score_gemma":0.001094547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001868044,"about_ca_topic_score_gemma":0.001191637,"domain_scores_codex":[0.9975516,0.0008687404,0.0002348929,0.0005253547,0.0007216009,0.00009781479],"domain_scores_gemma":[0.9969764,0.002074871,0.0002037248,0.0003013982,0.0003756715,0.000067967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008881652,0.000101335,0.002979552,0.001255921,0.0002626955,0.0006188665,0.0003469647,0.1425995,0.004952618,0.5216656,0.01111529,0.3140128],"study_design_scores_gemma":[0.00001767655,0.00008773433,0.0007253001,0.0002934966,0.00005624574,0.0006428022,0.00007775596,0.4705765,0.002484,0.4798894,0.04507598,0.00007314995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003385304,0.01505266,0.9735902,0.001102528,0.0003386878,0.00008019477,0.0002000363,0.0004008676,0.005849379],"genre_scores_gemma":[0.2604759,0.04651535,0.677963,0.001258239,0.001704018,0.0004867257,0.001035693,0.0003033063,0.01025774],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00297583,"threshold_uncertainty_score":0.009955108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01161813899330047,"score_gpt":0.2513580775131439,"score_spread":0.2397399385198435,"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."}}