{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001105426,0.0002306056,0.0004267786,0.0004852101,0.0005941545,0.0001096304,0.0006246459,0.0007019122,0.0005092436],"category_scores_gemma":[0.0001567974,0.0002754347,0.0003318166,0.000985007,0.0007310972,0.0001385161,0.0004387907,0.0005499723,0.00006106318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002371422,"about_ca_system_score_gemma":0.0002385639,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006340803,"about_ca_topic_score_gemma":0.02016884,"domain_scores_codex":[0.9978073,0.0005634989,0.0002026692,0.000960017,0.0001432543,0.0003232963],"domain_scores_gemma":[0.9982027,0.0002764029,0.0002141826,0.0008286779,0.0002377086,0.0002403141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000270938,0.001074356,0.07880221,0.0007021445,0.009083305,0.0002870193,0.04413108,0.03039452,0.000002126137,0.800909,0.002327096,0.03201622],"study_design_scores_gemma":[0.0007511564,0.0002154364,0.01324733,0.000700336,0.01283397,0.00000111272,0.0173468,0.08538415,0.00004105481,0.7321612,0.1343053,0.003012259],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6911857,0.003623534,0.2035815,0.004448832,0.0005723459,0.001638577,0.00008994003,0.001300526,0.09355904],"genre_scores_gemma":[0.9925138,0.004555809,0.0003824693,0.000122839,0.0004733698,0.000002824123,0.00002613162,0.00001345956,0.001909242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3013282,"threshold_uncertainty_score":0.9999698,"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."}}