{"id":"W4402035557","doi":"10.32920/26882485","title":"Graph and Semantic Analysis Approach for Template Recognition in Large Scale Log Data","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Graph; Scale (ratio); Natural language processing; Artificial intelligence; Data mining; Pattern recognition (psychology); Information retrieval; Theoretical computer science; Cartography","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":[0.0009461467,0.0007658266,0.0006535648,0.00649959,0.000891633,0.001677982,0.001489288,0.001046128,0.002021946],"category_scores_gemma":[0.004807884,0.0004015354,0.001525657,0.004997884,0.0009556527,0.002773901,0.00117594,0.001363386,0.001103394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001036726,"about_ca_system_score_gemma":0.00189384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008142943,"about_ca_topic_score_gemma":0.01431573,"domain_scores_codex":[0.9988858,0.0001975842,0.00009979677,0.0003096046,0.0004316933,0.00007556094],"domain_scores_gemma":[0.9968046,0.001450341,0.000344125,0.000728314,0.0005762746,0.00009625979],"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":[0.0002256211,0.0005818836,0.01058007,0.0003984088,0.000188712,0.0008301368,0.0005628188,0.1249271,0.03034985,0.07313254,0.01472046,0.7435024],"study_design_scores_gemma":[0.00001175975,0.00003552779,0.001384523,0.00002137164,0.00002704158,0.0001793826,0.0001599357,0.920669,0.008374068,0.0625547,0.006555018,0.00002764449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008264501,0.00009126514,0.9860299,0.00018415,0.00002287153,0.00009018991,0.0007544447,0.003901696,0.0006610513],"genre_scores_gemma":[0.1238203,0.0002137403,0.8694755,0.0001451392,0.00004032599,0.0002052338,0.003720116,0.000534753,0.0018448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008142943,"threshold_uncertainty_score":0.01619112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07290353216277942,"score_gpt":0.3152091593200427,"score_spread":0.2423056271572633,"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."}}