{"id":"W2103475226","doi":"10.1111/j.1475-4991.2008.00272.x","title":"LIFETIMES OF MACHINERY AND EQUIPMENT: EVIDENCE FROM DUTCH MANUFACTURING","year":2008,"lang":"en","type":"article","venue":"Review of Income and Wealth","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Weibull distribution; Econometrics; Asset (computer security); Stock (firearms); Capital asset; Economics; Service (business); Manufacturing; Business; Statistics; Finance; Engineering; Computer science; Economy; Mathematics; Marketing","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":[0.001244922,0.0001062228,0.0004393475,0.00006893365,0.0001977789,0.000008707942,0.0001740602,0.00003583747,0.00006624107],"category_scores_gemma":[0.00009584043,0.00008793031,0.00007418604,0.000146561,0.0003163205,0.0001744581,0.0001023171,0.00008022306,0.000003144758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001872515,"about_ca_system_score_gemma":0.00006000748,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007216123,"about_ca_topic_score_gemma":0.0003251643,"domain_scores_codex":[0.9985582,0.000234495,0.0004476108,0.000200369,0.0003759364,0.0001833622],"domain_scores_gemma":[0.9991741,0.0001836488,0.0003012658,0.0002018284,0.00004308147,0.00009603766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001206048,0.0000571582,0.9284845,0.01123731,0.00007311886,0.000008997536,0.002787172,7.910839e-7,0.00002001622,0.001742744,0.0005590872,0.05501705],"study_design_scores_gemma":[0.0001600006,0.00005758853,0.9783231,0.01129652,0.00006402442,0.000001637877,0.000164195,0.000004414336,0.0001509099,0.0008054189,0.008823137,0.0001490498],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7386674,0.2581256,0.00001767862,0.0006577088,0.00007195007,0.0003323214,0.00001133512,0.00001350618,0.002102446],"genre_scores_gemma":[0.5294367,0.470043,0.000281833,0.0001599472,0.00003484197,0.000004195736,0.000001263471,0.000003143517,0.00003504438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2119174,"threshold_uncertainty_score":0.9993949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0293045639611985,"score_gpt":0.3239343820497526,"score_spread":0.2946298180885541,"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."}}