{"id":"W4408793665","doi":"10.1109/icctit64404.2024.10928538","title":"AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP","year":2024,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Architecture; Computer architecture; Geography","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":[0.0001228775,0.0001376427,0.0002711679,0.0001345854,0.00009471901,0.0002596628,0.0002938306,0.00004674478,0.000005547464],"category_scores_gemma":[0.00002023282,0.0001187177,0.00005397895,0.0003575731,0.00003647514,0.0003201847,0.0002988985,0.0001989454,0.00001439737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005744803,"about_ca_system_score_gemma":0.00009972759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007266605,"about_ca_topic_score_gemma":0.00007437011,"domain_scores_codex":[0.9986962,0.0001183848,0.00032522,0.0004338842,0.0002374959,0.0001887828],"domain_scores_gemma":[0.9989941,0.0004598102,0.0000844568,0.0002769331,0.00006243594,0.0001223252],"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":[0.000008925578,0.00008549215,0.01159046,0.0003258085,0.0001980798,0.00005174623,0.0101781,0.0002214264,0.00772883,0.007683124,0.0007689941,0.9611591],"study_design_scores_gemma":[0.001503771,0.0004991879,0.1055828,0.003939432,0.00003729295,0.000187437,0.0009657114,0.8211722,0.0312125,0.01517246,0.01841176,0.001315468],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1523778,0.001302754,0.8431526,0.001836723,0.0006652171,0.0001636249,0.00004028259,0.0003316243,0.0001293535],"genre_scores_gemma":[0.9912203,0.00001056137,0.00842335,0.0001319496,0.0001706665,0.000003052209,0.00001188643,0.000009102103,0.00001911424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9598436,"threshold_uncertainty_score":0.4841166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02557519988338085,"score_gpt":0.2836516780655526,"score_spread":0.2580764781821718,"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."}}