{"id":"W2910220164","doi":"10.48550/arxiv.1901.02704","title":"Cluster Lifecycle Analysis: Challenges, Techniques, and Framework","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Variety (cybernetics); Data science; Computer science; Cluster (spacecraft); Cluster analysis; Application lifecycle management; Identification (biology); Risk analysis (engineering); Domain (mathematical analysis); Resource (disambiguation); System lifecycle; Process management; Engineering; Business; Artificial intelligence","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":[0.01873301,0.001325577,0.002007223,0.008120614,0.002861376,0.01222289,0.006418577,0.002806018,0.001992666],"category_scores_gemma":[0.03209208,0.001392344,0.002379707,0.01116076,0.007383764,0.01400051,0.007172993,0.005031972,0.001331714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006146635,"about_ca_system_score_gemma":0.007450661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0180924,"about_ca_topic_score_gemma":0.009831021,"domain_scores_codex":[0.9887056,0.004928104,0.0007738787,0.001916587,0.00321029,0.0004656667],"domain_scores_gemma":[0.9763075,0.01215145,0.00135495,0.004135527,0.00498904,0.001061453],"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":[0.00006666861,0.0001276316,0.006613033,0.0009953808,0.0001679242,0.0001869056,0.001717284,0.04842145,0.0005205139,0.761851,0.01836859,0.1609637],"study_design_scores_gemma":[0.00001693728,0.00002605083,0.001418885,0.0003534516,0.00003467836,0.0001814932,0.00151146,0.2444032,0.0005206657,0.705365,0.04609772,0.00007057504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003503263,0.005616709,0.9763194,0.008855752,0.0002013221,0.0004208127,0.0007739771,0.0006728063,0.003635849],"genre_scores_gemma":[0.09654617,0.008307015,0.888498,0.0006734014,0.0007969067,0.001099729,0.001629663,0.0003787381,0.002070452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01873301,"threshold_uncertainty_score":0.09907079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08199882195769212,"score_gpt":0.2406502373303158,"score_spread":0.1586514153726236,"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."}}