{"id":"W3124463716","doi":"10.1111/j.1475-4991.2008.00272.x/enhancedabs","title":"Lifetimes of Machinery and Equipment. Evidence from Dutch Manufacturing","year":2006,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Weibull distribution; Asset (computer security); Stock (firearms); Econometrics; Electrical machinery; Service (business); Capital asset; Business; Economics; Engineering; Computer science; Statistics; Finance; Mathematics; Economy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004439923,0.0003067594,0.0006276473,0.0005944732,0.0003247922,0.0002601368,0.001054413,0.0003952829,0.0001382988],"category_scores_gemma":[0.0003884215,0.000346414,0.0001969313,0.0001290549,0.001111124,0.0002303955,0.001768989,0.001130853,0.000005846797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005779781,"about_ca_system_score_gemma":0.0003798209,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03503364,"about_ca_topic_score_gemma":0.02012806,"domain_scores_codex":[0.9958487,0.0007764293,0.0008037039,0.001026236,0.0007127021,0.0008321914],"domain_scores_gemma":[0.997409,0.00108301,0.000353119,0.0008846647,0.00009215253,0.0001780108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000110629,0.0002070437,0.7071748,0.0005050882,0.0002791787,0.00005526141,0.004383992,0.005122594,0.00005805071,0.0007864892,0.0003508047,0.2809661],"study_design_scores_gemma":[0.000579513,0.00006472985,0.9592289,0.001363623,0.00005177536,6.113821e-7,0.002774419,0.001411691,0.0005775827,0.01548792,0.01756939,0.0008898107],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9044812,0.001181036,0.000005070888,0.0004601158,0.0004609513,0.0009062453,0.00008424084,0.00004354379,0.09237759],"genre_scores_gemma":[0.9628481,0.03415684,0.001012292,0.00003315191,0.0003622938,0.0001170745,0.00003176303,0.00003862912,0.001399915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2800763,"threshold_uncertainty_score":0.9998988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03999065294170723,"score_gpt":0.3460914688402045,"score_spread":0.3061008158984973,"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."}}