{"id":"W2162121999","doi":"10.1109/ride.2005.11","title":"Maintaining Knowledge-Bases of Navigational Patterns from Streams of Navigational Sequences","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Sliding window protocol; Window (computing); Data mining; Computation; Sequential Pattern Mining; Data stream mining; Real-time computing; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001488551,0.00008866078,0.0001311418,0.00004580286,0.00005945776,0.00003422249,0.0006019509,0.00003238662,0.0002063926],"category_scores_gemma":[0.00001905607,0.00007930648,0.00004749688,0.0001769108,0.0000817934,0.0003848898,0.0001424606,0.00006290348,0.0000270134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002320787,"about_ca_system_score_gemma":0.0001362223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004807079,"about_ca_topic_score_gemma":0.00005483446,"domain_scores_codex":[0.999033,0.00002381702,0.0003283524,0.0002508137,0.0002453288,0.0001186595],"domain_scores_gemma":[0.9990563,0.0002648004,0.0001575642,0.0003146997,0.0001534823,0.00005318568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002783178,0.0003240229,0.01462901,0.00001429272,0.00005109664,0.000001007089,0.001173915,0.001012756,0.003062434,0.3870946,0.0008519632,0.5917822],"study_design_scores_gemma":[0.00105373,0.0002028449,0.063967,0.0005112595,0.00003336004,0.0000155727,0.0009292631,0.7465166,0.145135,0.0323598,0.008652379,0.0006231331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5021359,0.00008841791,0.4936331,0.0009334721,0.00006699188,0.00007976829,0.0005301519,0.00006143738,0.002470842],"genre_scores_gemma":[0.7481051,0.000004914142,0.2514368,0.00004949371,0.0001086304,0.00001240064,0.0001966153,0.00000327715,0.00008276712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7455038,"threshold_uncertainty_score":0.3234024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01759193110172354,"score_gpt":0.2817929662798486,"score_spread":0.264201035178125,"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."}}