{"id":"W4400066539","doi":"10.32372/chjs.15-01-05","title":"Unveiling patterns and trends in research on cumulative damage models for statistical and reliability analyses: Bibliometric and thematic explorations with data analytics","year":2024,"lang":"en","type":"article","venue":"Chilean Journal of Statistics","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"CHIST-ERA; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Agencia Nacional de Investigación y Desarrollo; Universidade do Minho; Agenția Națională pentru Cercetare și Dezvoltare","keywords":"Reliability (semiconductor); Thematic map; Data science; Analytics; Computer science; Data analysis; Statistics; Data mining; Geography; Cartography; Mathematics; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001423388,0.0001230841,0.0002671905,0.004496218,0.00006689681,0.0001960189,0.00009986402,0.00004304516,0.000009828897],"category_scores_gemma":[0.0006209478,0.00009196829,0.0000107998,0.004308779,0.0001527702,0.000486948,0.00004323036,0.0003548805,1.598588e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005614996,"about_ca_system_score_gemma":0.00003352278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001063989,"about_ca_topic_score_gemma":0.00003778011,"domain_scores_codex":[0.9988244,0.00008343505,0.0004397981,0.0002180804,0.0002546751,0.0001795957],"domain_scores_gemma":[0.9974509,0.001956546,0.00005346616,0.0002090316,0.0002206978,0.0001093138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008791279,0.00007410619,0.0005726134,0.001075229,0.000101283,0.00004673125,0.00158235,0.9492958,0.000007202736,0.009759478,0.001196618,0.03620065],"study_design_scores_gemma":[0.0003646695,0.0003579526,0.005335038,0.0003946916,0.00007882753,0.00001461598,0.0009978758,0.9746607,0.000005944875,0.01768045,0.00001304474,0.00009618366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1309312,0.0003405504,0.867349,0.00007883738,0.00003522618,0.0001321102,0.001068965,0.00001109221,0.00005299479],"genre_scores_gemma":[0.9085022,0.001755644,0.08956367,0.000004584369,0.00002948505,0.000003596432,0.0001100414,0.00002174293,0.000009026364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7777853,"threshold_uncertainty_score":0.4011929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3249776292588378,"score_gpt":0.4433852932072785,"score_spread":0.1184076639484408,"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."}}